Packages and custom functions

The lnRR_func function is here used to calculate a log response ratio (lnRR) adjusted for small sample sizes. In addition, this formula accounts for correlated samples. For more details, see Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644

# packages
library(tidyverse)
library(googlesheets4)
library(here)
library(metafor)
library(metaAidR)  # see a note above
library(orchaRd)  # see a note above
library(ape)
library(clubSandwich)
library(metaAidR)
library(patchwork)
library(emmeans)  # see a note above
library(kableExtra)
library(GGally)
library(cowplot)
library(grDevices)  # reqired for using base and ggplots together

# Below is the custom function to calculate the lnRR
lnRR_func <- function(Mc, Nc, Me, Ne, aCV2c, aCV2e, rho = 0.5) {
    lnRR <- log(Me/Mc) + 0.5 * ((aCV2e/Ne) - (aCV2c/Nc))

    var_lnRR <- (aCV2c/Nc) + (aCV2e/Ne) - 2 * rho * ((aCV2c * aCV2e)/sqrt(Nc * Ne))

    data.frame(lnRR, var_lnRR)
}

# Mc: Concentration of PFAS of the raw (control) sample Nc: Sample size of the
# raw (control) sample Me: Concentration of PFAS of the cooked (experimental)
# sample Ne: Sample size of the cooked (experimental) sample aCV2c: Mean
# coefficient of variation of the raw (control) samples aCV2e: Mean coefficient
# of variation of the cooked (experimental) samples

Data import and processing

Import and process raw data

Import raw data

raw_data <- read_sheet("https://docs.google.com/spreadsheets/d/1cbmYDfIc2dxHJxBaowojUZZkN31NW4sL_pHw0t9eTTU/edit#gid=477880397",
    range = "Data_extraction_2", skip = 1, col_types = "ccncccccncncccccnncccnccnncncnccnnncncncccccccc")  # Import raw data

Process raw data

processed_data <- filter(raw_data, !PFAS_type == "PFOS_Total")
processed_data <- filter(processed_data, !Species_common == "Fish cake")

write.csv(processed_data, here("data", "Rawdata.csv"), row.names = F)

Load processed data

processed_data <- read.csv(here("data", "Rawdata.csv")) 

dat <- processed_data %>% mutate(SDc = ifelse(Sc_technical_biological == "biological", Sc, NA), # Calculate the SD of biological replicates for control samples
                                 SDe = ifelse(Se_technical_biological == "biological", Se, NA)) # Calculate the SD of biological replicates for experimental samples

#### Ratio_liquid_fish with "0" for the dry cooking category

dat<-dat %>% mutate(Ratio_liquid_fish_0 = ifelse(Cooking_Category =="No liquid", 0, Ratio_liquid_fish)) # Add a 0 when the cooking category is "No liquid", otherwise keep the same value of Ratio_liquid_fish

# arrange(select(dat, Cooking_Category, Ratio_liquid_fish, Ratio_liquid_fish_0), Cooking_Category) # Checking everything is fine



kable(dat, "html") %>% kable_styling("striped", position = "left") %>% scroll_box(width = "100%", height = "500px")
Study_ID Author_year Publication_year Country_firstAuthor Effect_ID Species_common Species_Scientific Invertebrate_vertebrate Fish_mollusc Moisture_loss_in_percent PFAS_type PFAS_carbon_chain linear_total Choice_of_9 Cooking_method Cooking_Category Comments_cooking Temperature_in_Celsius Length_cooking_time_in_s Water Oil Oil_type Volume_liquid_ml Volume_liquid_ml_0 Ratio_liquid_fish Weigh_g_sample Cohort_ID Cohort_comment Nc Pooled_Nc Unit_PFAS_conc Mc Mc_comment Sc sd Sc_technical_biological Ne Pooled_Ne Me Me_comment Se Se_technical_biological If_technical_how_many Unit_LOD_LOQ LOD LOQ Design DataSource Raw_data_provided General_comments checked SDc SDe Ratio_liquid_fish_0
F001 Alves_2017 2017 Portugal E001 Flounder Platichthys flesus vertebrate marine fish 7.430000 PFOS 8 linear Yes Steaming water-based NA 105 900 Yes No NA NA NA NA NA C001 NA 25 1 ng/g 24.0000000 NA 1.5280000 sd technical 25 1 22.0000000 NA 1.5300000 technical 2 ng/g <0.1 <0.2 Dependent Table 3 No Authors replied ML - ok NA NA NA
F001 Alves_2017 2017 Portugal E002 Mackerel Scomber scombrus vertebrate marine fish NA PFUnDA 11 NA Yes Steaming water-based NA 105 900 Yes No NA NA NA NA NA C002 NA 25 1 ng/g 3.1000000 NA 0.2120000 sd technical 25 1 2.9000000 NA 0.1410000 technical 2 ng/g <0.1 <0.2 Dependent Table 3 No Authors replied ML - ok NA NA NA
F002 Barbosa_2018 2018 Portugal E003 Skipjack tuna Katsuwonus pelamis vertebrate marine fish 16.860000 PFUnDA 11 NA Yes Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA NA NA NA C003 NA 25 1 ng/g 13.3018868 NA 0.0471698 sd technical 25 1 4.1509434 NA 0.0943396 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA NA
F002 Barbosa_2018 2018 Portugal E004 Skipjack tuna Katsuwonus pelamis vertebrate marine fish 16.860000 PFDoDA 12 NA No Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA NA NA NA C003 NA 25 1 ng/g 3.5731707 NA 0.0243902 sd technical 25 1 3.2073171 NA 0.0243902 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA NA
F002 Barbosa_2018 2018 Portugal E005 Skipjack tuna Katsuwonus pelamis vertebrate marine fish 16.860000 PFTrA 13 NA No Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA NA NA NA C003 NA 25 1 ng/g 6.5283019 NA 0.0754717 sd technical 25 1 10.0377358 NA 0.0754717 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA NA
F002 Barbosa_2018 2018 Portugal E006 Skipjack tuna Katsuwonus pelamis vertebrate marine fish 16.860000 PFTA 14 NA No Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA NA NA NA C003 NA 25 1 ng/g 1.3736842 NA 0.0157895 sd technical 25 1 1.3315789 NA 0.0210526 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA NA
F002 Barbosa_2018 2018 Portugal E007 Skipjack tuna Katsuwonus pelamis vertebrate marine fish 16.860000 PFOS 8 total Yes Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA NA NA NA C003 NA 25 1 ng/g 0.6467391 NA 0.0054348 sd technical 25 1 0.3016304 NA 0.0081522 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA NA
F002 Barbosa_2018 2018 Portugal E008 Skipjack tuna Katsuwonus pelamis vertebrate marine fish 16.860000 PFDA 10 NA Yes Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA NA NA NA C003 NA 25 1 ng/g 0.0250000 <LOQ NA sd technical 25 1 0.0869767 NA 0.0130233 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA NA
F002 Barbosa_2018 2018 Portugal E009 European plaice Pleuronectes platessa vertebrate marine fish 8.700000 PFOS 8 total Yes Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA NA NA NA C004 NA 25 1 ng/g 0.2472826 NA 0.0081522 sd technical 25 1 0.2527174 NA 0.0054348 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA NA
F002 Barbosa_2018 2018 Portugal E010 blue mussel Mytilus edulis invertebrate mollusca 6.770000 PFBA 3 NA No Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA NA NA NA C005 NA 50 1 ng/g 0.0250000 <LOQ NA sd technical 50 1 0.2083333 NA 0.0090909 technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA NA
F002 Barbosa_2018 2018 Portugal E011 blue mussel Mytilus edulis invertebrate mollusca 6.770000 PFDA 10 NA Yes Steaming water-based wrapped up in aluminum foil 105 900 Yes No NA NA NA NA NA C005 NA 50 1 ng/g 0.0241860 NA 0.0074419 sd technical 50 1 0.0250000 <LOQ NA technical 2 ng/g <0.01 <0.04 Dependent Table 2 Yes Authors replied NA NA NA NA
F003 Bhavsar_2014 2014 Canada E012 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFNA 9 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1042160 105.5500 C006 NA 5 5 ng/g 0.0670000 NA 0.0950000 sd biological 5 5 0.0860000 NA 0.1350000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. ML 0.0950000 0.1350000 0.1042160
F003 Bhavsar_2014 2014 Canada E013 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFDA 10 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1042160 105.5500 C006 NA 5 5 ng/g 0.1560000 NA 0.1970000 sd biological 5 5 0.1920000 NA 0.2660000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1970000 0.2660000 0.1042160
F003 Bhavsar_2014 2014 Canada E014 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFUnDA 11 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1042160 105.5500 C006 NA 5 5 ng/g 0.1860000 NA 0.2250000 sd biological 5 5 0.2340000 NA 0.2910000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2250000 0.2910000 0.1042160
F003 Bhavsar_2014 2014 Canada E015 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFDoDA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1042160 105.5500 C006 NA 5 5 ng/g 0.0800000 NA 0.0730000 sd biological 5 5 0.1010000 NA 0.0950000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0730000 0.0950000 0.1042160
F003 Bhavsar_2014 2014 Canada E016 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFTrA 13 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1042160 105.5500 C006 NA 5 5 ng/g 0.2150000 NA 0.1830000 sd biological 5 5 0.2590000 NA 0.2410000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1830000 0.2410000 0.1042160
F003 Bhavsar_2014 2014 Canada E017 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFTA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1042160 105.5500 C006 NA 5 5 ng/g 0.0760000 NA 0.0550000 sd biological 5 5 0.0830000 NA 0.0730000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0550000 0.0730000 0.1042160
F003 Bhavsar_2014 2014 Canada E019 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFOS 8 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1042160 105.5500 C006 NA 5 5 ng/g 12.7000000 NA 12.6100000 sd biological 5 5 16.5600000 NA 18.0000000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 12.6100000 18.0000000 0.1042160
F003 Bhavsar_2014 2014 Canada E020 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 PFDS 10 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1042160 105.5500 C006 NA 5 5 ng/g 0.3030000 NA 0.2840000 sd biological 5 5 0.3970000 NA 0.4330000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2840000 0.4330000 0.1042160
F003 Bhavsar_2014 2014 Canada E021 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 6:6PFPIA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1042160 105.5500 C006 NA 5 5 ng/g 0.0030000 NA 0.0030000 sd biological 5 5 0.0020000 NA 0.0020000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0030000 0.0020000 0.1042160
F003 Bhavsar_2014 2014 Canada E022 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 14.400000 6:8PFPIA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1042160 105.5500 C006 NA 5 5 ng/g 0.0170000 NA 0.0230000 sd biological 5 5 0.0100000 NA 0.0160000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0230000 0.0160000 0.1042160
F003 Bhavsar_2014 2014 Canada E023 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFNA 9 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0033813 100.8500 C007 NA 5 5 ng/g 0.0670000 NA 0.0950000 sd biological 5 5 0.0830000 NA 0.1180000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0950000 0.1180000 0.0033813
F003 Bhavsar_2014 2014 Canada E024 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFDA 10 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0033813 100.8500 C007 NA 5 5 ng/g 0.1560000 NA 0.1970000 sd biological 5 5 0.1900000 NA 0.2320000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1970000 0.2320000 0.0033813
F003 Bhavsar_2014 2014 Canada E025 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFUnDA 11 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0033813 100.8500 C007 NA 5 5 ng/g 0.1860000 NA 0.2250000 sd biological 5 5 0.2560000 NA 0.3100000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2250000 0.3100000 0.0033813
F003 Bhavsar_2014 2014 Canada E026 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFDoDA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0033813 100.8500 C007 NA 5 5 ng/g 0.0800000 NA 0.0730000 sd biological 5 5 0.1000000 NA 0.0800000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0730000 0.0800000 0.0033813
F003 Bhavsar_2014 2014 Canada E027 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFTrA 13 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0033813 100.8500 C007 NA 5 5 ng/g 0.2150000 NA 0.1830000 sd biological 5 5 0.2850000 NA 0.2340000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1830000 0.2340000 0.0033813
F003 Bhavsar_2014 2014 Canada E028 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFTA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0033813 100.8500 C007 NA 5 5 ng/g 0.0760000 NA 0.0550000 sd biological 5 5 0.0830000 NA 0.0710000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0550000 0.0710000 0.0033813
F003 Bhavsar_2014 2014 Canada E030 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFOS 8 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0033813 100.8500 C007 NA 5 5 ng/g 12.7000000 NA 12.6100000 sd biological 5 5 16.4500000 NA 15.6300000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 12.6100000 15.6300000 0.0033813
F003 Bhavsar_2014 2014 Canada E031 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 PFDS 10 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0033813 100.8500 C007 NA 5 5 ng/g 0.3030000 NA 0.2840000 sd biological 5 5 0.3920000 NA 0.3590000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2840000 0.3590000 0.0033813
F003 Bhavsar_2014 2014 Canada E032 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 6:6PFPIA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0033813 100.8500 C007 NA 5 5 ng/g 0.0030000 NA 0.0030000 sd biological 5 5 0.0020000 NA 0.0030000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0030000 0.0030000 0.0033813
F003 Bhavsar_2014 2014 Canada E033 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 19.680000 6:8PFPIA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0033813 100.8500 C007 NA 5 5 ng/g 0.0170000 NA 0.0230000 sd biological 5 5 0.0140000 NA 0.0220000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0230000 0.0220000 0.0033813
F003 Bhavsar_2014 2014 Canada E034 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFNA 9 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1000364 109.9600 C008 NA 5 5 ng/g 0.0670000 NA 0.0950000 sd biological 5 5 0.0780000 NA 0.1140000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0950000 0.1140000 0.1000364
F003 Bhavsar_2014 2014 Canada E035 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFDA 10 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1000364 109.9600 C008 NA 5 5 ng/g 0.1560000 NA 0.1970000 sd biological 5 5 0.1820000 NA 0.2220000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1970000 0.2220000 0.1000364
F003 Bhavsar_2014 2014 Canada E036 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFUnDA 11 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1000364 109.9600 C008 NA 5 5 ng/g 0.1860000 NA 0.2250000 sd biological 5 5 0.2270000 NA 0.2550000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2250000 0.2550000 0.1000364
F003 Bhavsar_2014 2014 Canada E037 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFDoDA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1000364 109.9600 C008 NA 5 5 ng/g 0.0800000 NA 0.0730000 sd biological 5 5 0.0960000 NA 0.0810000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0730000 0.0810000 0.1000364
F003 Bhavsar_2014 2014 Canada E038 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFTrA 13 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1000364 109.9600 C008 NA 5 5 ng/g 0.2150000 NA 0.1830000 sd biological 5 5 0.2750000 NA 0.2160000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1830000 0.2160000 0.1000364
F003 Bhavsar_2014 2014 Canada E039 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFTA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1000364 109.9600 C008 NA 5 5 ng/g 0.0760000 NA 0.0550000 sd biological 5 5 0.0870000 NA 0.0670000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0550000 0.0670000 0.1000364
F003 Bhavsar_2014 2014 Canada E041 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFOS 8 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1000364 109.9600 C008 NA 5 5 ng/g 12.7000000 NA 12.6100000 sd biological 5 5 16.0300000 NA 15.1900000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 12.6100000 15.1900000 0.1000364
F003 Bhavsar_2014 2014 Canada E042 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 PFDS 10 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1000364 109.9600 C008 NA 5 5 ng/g 0.3030000 NA 0.2840000 sd biological 5 5 0.3930000 NA 0.3690000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2840000 0.3690000 0.1000364
F003 Bhavsar_2014 2014 Canada E043 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 6:6PFPIA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1000364 109.9600 C008 NA 5 5 ng/g 0.0030000 NA 0.0030000 sd biological 5 5 0.0020000 NA 0.0030000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0030000 0.0030000 0.1000364
F003 Bhavsar_2014 2014 Canada E044 Chinook salmon Oncorhynchus tshawytscha vertebrate marine fish 18.680000 6:8PFPIA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1000364 109.9600 C008 NA 5 5 ng/g 0.0170000 NA 0.0230000 sd biological 5 5 0.0130000 NA 0.0220000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0230000 0.0220000 0.1000364
F003 Bhavsar_2014 2014 Canada E045 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFNA 9 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1114940 98.6600 C009 NA 5 5 ng/g 0.0920000 NA 0.0300000 sd biological 5 5 0.0990000 NA 0.0220000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0300000 0.0220000 0.1114940
F003 Bhavsar_2014 2014 Canada E046 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFDA 10 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1114940 98.6600 C009 NA 5 5 ng/g 0.5180000 NA 0.1070000 sd biological 5 5 0.5660000 NA 0.1380000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1070000 0.1380000 0.1114940
F003 Bhavsar_2014 2014 Canada E047 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFUnDA 11 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1114940 98.6600 C009 NA 5 5 ng/g 0.7120000 NA 0.1580000 sd biological 5 5 0.8040000 NA 0.1670000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1580000 0.1670000 0.1114940
F003 Bhavsar_2014 2014 Canada E048 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFDoDA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1114940 98.6600 C009 NA 5 5 ng/g 0.9890000 NA 0.3170000 sd biological 5 5 1.0960000 NA 0.3960000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.3170000 0.3960000 0.1114940
F003 Bhavsar_2014 2014 Canada E049 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFTrA 13 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1114940 98.6600 C009 NA 5 5 ng/g 0.7790000 NA 0.4400000 sd biological 5 5 0.7740000 NA 0.3320000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.4400000 0.3320000 0.1114940
F003 Bhavsar_2014 2014 Canada E050 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFTA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1114940 98.6600 C009 NA 5 5 ng/g 0.9510000 NA 0.6470000 sd biological 5 5 1.1400000 NA 0.8740000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.6470000 0.8740000 0.1114940
F003 Bhavsar_2014 2014 Canada E051 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFHxS 6 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1114940 98.6600 C009 NA 5 5 ng/g 0.2920000 NA 0.3190000 sd biological 5 5 0.3410000 NA 0.3910000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.3190000 0.3910000 0.1114940
F003 Bhavsar_2014 2014 Canada E052 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFOS 8 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1114940 98.6600 C009 NA 5 5 ng/g 27.1700000 NA 7.7680000 sd biological 5 5 30.5200000 NA 9.2540000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 7.7680000 9.2540000 0.1114940
F003 Bhavsar_2014 2014 Canada E053 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 PFDS 10 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1114940 98.6600 C009 NA 5 5 ng/g 0.9110000 NA 0.5320000 sd biological 5 5 1.0840000 NA 0.5710000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.5320000 0.5710000 0.1114940
F003 Bhavsar_2014 2014 Canada E054 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 6:6PFPIA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1114940 98.6600 C009 NA 5 5 ng/g 0.0980000 NA 0.0600000 sd biological 5 5 0.1050000 NA 0.0600000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0600000 0.0600000 0.1114940
F003 Bhavsar_2014 2014 Canada E055 Common carp Cyprinus carpio vertebrate freshwater fish 15.470000 6:8PFPIA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.1114940 98.6600 C010 NA 5 5 ng/g 0.1670000 NA 0.0770000 sd biological 5 5 0.1800000 NA 0.0840000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0770000 0.0840000 0.1114940
F003 Bhavsar_2014 2014 Canada E056 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFNA 9 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0034647 98.4200 C010 NA 5 5 ng/g 0.0920000 NA 0.0300000 sd biological 5 5 0.1050000 NA 0.0370000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0300000 0.0370000 0.0034647
F003 Bhavsar_2014 2014 Canada E057 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFDA 10 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0034647 98.4200 C010 NA 5 5 ng/g 0.5180000 NA 0.1070000 sd biological 5 5 0.5480000 NA 0.1210000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1070000 0.1210000 0.0034647
F003 Bhavsar_2014 2014 Canada E058 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFUnDA 11 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0034647 98.4200 C010 NA 5 5 ng/g 0.7120000 NA 0.1580000 sd biological 5 5 0.8480000 NA 0.1550000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1580000 0.1550000 0.0034647
F003 Bhavsar_2014 2014 Canada E059 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFDoDA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0034647 98.4200 C010 NA 5 5 ng/g 0.9890000 NA 0.3170000 sd biological 5 5 1.1080000 NA 0.4040000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.3170000 0.4040000 0.0034647
F003 Bhavsar_2014 2014 Canada E060 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFTrA 13 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0034647 98.4200 C010 NA 5 5 ng/g 0.7790000 NA 0.4400000 sd biological 5 5 0.8280000 NA 0.4180000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.4400000 0.4180000 0.0034647
F003 Bhavsar_2014 2014 Canada E061 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFTA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0034647 98.4200 C010 NA 5 5 ng/g 0.9510000 NA 0.6470000 sd biological 5 5 1.1150000 NA 0.7690000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.6470000 0.7690000 0.0034647
F003 Bhavsar_2014 2014 Canada E062 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFHxS 6 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0034647 98.4200 C010 NA 5 5 ng/g 0.2920000 NA 0.3190000 sd biological 5 5 0.2910000 NA 0.3460000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.3190000 0.3460000 0.0034647
F003 Bhavsar_2014 2014 Canada E063 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFOS 8 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0034647 98.4200 C010 NA 5 5 ng/g 27.1700000 NA 7.7680000 sd biological 5 5 28.3700000 NA 11.9900000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 7.7680000 11.9900000 0.0034647
F003 Bhavsar_2014 2014 Canada E064 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 PFDS 10 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0034647 98.4200 C010 NA 5 5 ng/g 0.9110000 NA 0.5320000 sd biological 5 5 1.0450000 NA 0.6230000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.5320000 0.6230000 0.0034647
F003 Bhavsar_2014 2014 Canada E065 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 6:6PFPIA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0034647 98.4200 C010 NA 5 5 ng/g 0.0980000 NA 0.0600000 sd biological 5 5 0.1170000 NA 0.0730000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0600000 0.0730000 0.0034647
F003 Bhavsar_2014 2014 Canada E066 Common carp Cyprinus carpio vertebrate freshwater fish 19.680000 6:8PFPIA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.341 0.341 0.0034647 98.4200 C010 NA 5 5 ng/g 0.1670000 NA 0.0770000 sd biological 5 5 0.1900000 NA 0.0800000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0770000 0.0800000 0.0034647
F003 Bhavsar_2014 2014 Canada E067 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFNA 9 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1099340 100.0600 C011 NA 5 5 ng/g 0.0920000 NA 0.0300000 sd biological 5 5 0.1010000 NA 0.0350000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0300000 0.0350000 0.1099340
F003 Bhavsar_2014 2014 Canada E068 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFDA 10 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1099340 100.0600 C011 NA 5 5 ng/g 0.5180000 NA 0.1070000 sd biological 5 5 0.5690000 NA 0.1080000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1070000 0.1080000 0.1099340
F003 Bhavsar_2014 2014 Canada E069 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFUnDA 11 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1099340 100.0600 C011 NA 5 5 ng/g 0.7120000 NA 0.1580000 sd biological 5 5 0.8300000 NA 0.1300000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1580000 0.1300000 0.1099340
F003 Bhavsar_2014 2014 Canada E070 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFDoDA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1099340 100.0600 C011 NA 5 5 ng/g 0.9890000 NA 0.3170000 sd biological 5 5 1.0440000 NA 0.3560000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.3170000 0.3560000 0.1099340
F003 Bhavsar_2014 2014 Canada E071 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFTrA 13 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1099340 100.0600 C011 NA 5 5 ng/g 0.7790000 NA 0.4400000 sd biological 5 5 0.7460000 NA 0.2830000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.4400000 0.2830000 0.1099340
F003 Bhavsar_2014 2014 Canada E072 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFTA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1099340 100.0600 C011 NA 5 5 ng/g 0.9510000 NA 0.6470000 sd biological 5 5 1.0670000 NA 0.7540000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.6470000 0.7540000 0.1099340
F003 Bhavsar_2014 2014 Canada E073 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFHxS 6 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1099340 100.0600 C011 NA 5 5 ng/g 0.2920000 NA 0.3190000 sd biological 5 5 0.3590000 NA 0.4280000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.3190000 0.4280000 0.1099340
F003 Bhavsar_2014 2014 Canada E074 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFOS 8 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1099340 100.0600 C011 NA 5 5 ng/g 27.1700000 NA 7.7680000 sd biological 5 5 28.1100000 NA 10.9300000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 7.7680000 10.9300000 0.1099340
F003 Bhavsar_2014 2014 Canada E075 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 PFDS 10 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1099340 100.0600 C011 NA 5 5 ng/g 0.9110000 NA 0.5320000 sd biological 5 5 1.0900000 NA 0.6180000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.5320000 0.6180000 0.1099340
F003 Bhavsar_2014 2014 Canada E076 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 6:6PFPIA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1099340 100.0600 C011 NA 5 5 ng/g 0.0980000 NA 0.0600000 sd biological 5 5 0.1060000 NA 0.0650000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0600000 0.0650000 0.1099340
F003 Bhavsar_2014 2014 Canada E077 Common carp Cyprinus carpio vertebrate freshwater fish 14.910000 6:8PFPIA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.1099340 100.0600 C011 NA 5 5 ng/g 0.1670000 NA 0.0770000 sd biological 5 5 0.1880000 NA 0.0750000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0770000 0.0750000 0.1099340
F003 Bhavsar_2014 2014 Canada E078 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFNA 9 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0517671 212.4900 C012 NA 4 4 ng/g 0.2980000 NA 0.1430000 sd biological 4 4 0.3700000 NA 0.1890000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1430000 0.1890000 0.0517671
F003 Bhavsar_2014 2014 Canada E079 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFDA 10 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0517671 212.4900 C012 NA 4 4 ng/g 0.4230000 NA 0.1860000 sd biological 4 4 0.5100000 NA 0.2320000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1860000 0.2320000 0.0517671
F003 Bhavsar_2014 2014 Canada E080 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFUnDA 11 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0517671 212.4900 C012 NA 4 4 ng/g 0.5600000 NA 0.2510000 sd biological 4 4 0.6850000 NA 0.2930000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2510000 0.2930000 0.0517671
F003 Bhavsar_2014 2014 Canada E081 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFDoDA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0517671 212.4900 C012 NA 4 4 ng/g 0.1980000 NA 0.0950000 sd biological 4 4 0.2210000 NA 0.1140000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0950000 0.1140000 0.0517671
F003 Bhavsar_2014 2014 Canada E082 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFTrA 13 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0517671 212.4900 C012 NA 4 4 ng/g 0.4610000 NA 0.2170000 sd biological 4 4 0.4840000 NA 0.2640000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2170000 0.2640000 0.0517671
F003 Bhavsar_2014 2014 Canada E083 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFTA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0517671 212.4900 C012 NA 4 4 ng/g 0.1280000 NA 0.0510000 sd biological 4 4 0.1370000 NA 0.0510000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0510000 0.0510000 0.0517671
F003 Bhavsar_2014 2014 Canada E084 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFHxS 6 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0517671 212.4900 C012 NA 4 4 ng/g 0.2580000 NA 0.0550000 sd biological 4 4 0.2480000 NA 0.0610000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0550000 0.0610000 0.0517671
F003 Bhavsar_2014 2014 Canada E085 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFOS 8 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0517671 212.4900 C012 NA 4 4 ng/g 18.1800000 NA 6.6860000 sd biological 4 4 20.5100000 NA 6.7520000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 6.6860000 6.7520000 0.0517671
F003 Bhavsar_2014 2014 Canada E086 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 PFDS 10 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0517671 212.4900 C012 NA 4 4 ng/g 0.4560000 NA 0.1770000 sd biological 4 4 0.4740000 NA 0.1960000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1770000 0.1960000 0.0517671
F003 Bhavsar_2014 2014 Canada E087 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 6:6PFPIA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0517671 212.4900 C012 NA 4 4 ng/g 0.0020000 NA 0.0010000 sd biological 4 4 0.0020000 NA 0.0020000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0010000 0.0020000 0.0517671
F003 Bhavsar_2014 2014 Canada E088 Lake trout Salvelinus namaycush vertebrate freshwater fish 10.130000 6:8PFPIA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0517671 212.4900 C012 NA 4 4 ng/g 0.0170000 NA 0.0090000 sd biological 4 4 0.0180000 NA 0.0090000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0090000 0.0090000 0.0517671
F003 Bhavsar_2014 2014 Canada E089 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFNA 9 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.528 0.528 0.0026586 198.6000 C013 NA 4 4 ng/g 0.2980000 NA 0.1430000 sd biological 4 4 0.3580000 NA 0.1700000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1430000 0.1700000 0.0026586
F003 Bhavsar_2014 2014 Canada E090 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFDA 10 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.528 0.528 0.0026586 198.6000 C013 NA 4 4 ng/g 0.4230000 NA 0.1860000 sd biological 4 4 0.5280000 NA 0.2330000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1860000 0.2330000 0.0026586
F003 Bhavsar_2014 2014 Canada E091 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFUnDA 11 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.528 0.528 0.0026586 198.6000 C013 NA 4 4 ng/g 0.5600000 NA 0.2510000 sd biological 4 4 0.7250000 NA 0.3450000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2510000 0.3450000 0.0026586
F003 Bhavsar_2014 2014 Canada E092 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFDoDA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.528 0.528 0.0026586 198.6000 C013 NA 4 4 ng/g 0.1980000 NA 0.0950000 sd biological 4 4 0.2370000 NA 0.1110000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0950000 0.1110000 0.0026586
F003 Bhavsar_2014 2014 Canada E093 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFTrA 13 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.528 0.528 0.0026586 198.6000 C013 NA 4 4 ng/g 0.4610000 NA 0.2170000 sd biological 4 4 0.5580000 NA 0.2800000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2170000 0.2800000 0.0026586
F003 Bhavsar_2014 2014 Canada E094 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFTA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.528 0.528 0.0026586 198.6000 C013 NA 4 4 ng/g 0.1280000 NA 0.0510000 sd biological 4 4 0.1490000 NA 0.0680000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0510000 0.0680000 0.0026586
F003 Bhavsar_2014 2014 Canada E095 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFHxS 6 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.528 0.528 0.0026586 198.6000 C013 NA 4 4 ng/g 0.2580000 NA 0.0550000 sd biological 4 4 0.2630000 NA 0.0870000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0550000 0.0870000 0.0026586
F003 Bhavsar_2014 2014 Canada E096 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFOS 8 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.528 0.528 0.0026586 198.6000 C013 NA 4 4 ng/g 18.1800000 NA 6.6860000 sd biological 4 4 22.1100000 NA 7.8970000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 6.6860000 7.8970000 0.0026586
F003 Bhavsar_2014 2014 Canada E097 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 PFDS 10 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.528 0.528 0.0026586 198.6000 C013 NA 4 4 ng/g 0.4560000 NA 0.1770000 sd biological 4 4 0.5600000 NA 0.2260000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1770000 0.2260000 0.0026586
F003 Bhavsar_2014 2014 Canada E098 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 6:6PFPIA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.528 0.528 0.0026586 198.6000 C013 NA 4 4 ng/g 0.0020000 NA 0.0010000 sd biological 4 4 0.0120000 NA 0.0180000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0010000 0.0180000 0.0026586
F003 Bhavsar_2014 2014 Canada E099 Lake trout Salvelinus namaycush vertebrate freshwater fish 15.230000 6:8PFPIA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.528 0.528 0.0026586 198.6000 C013 NA 4 4 ng/g 0.0170000 NA 0.0090000 sd biological 4 4 0.0160000 NA 0.0060000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0090000 0.0060000 0.0026586
F003 Bhavsar_2014 2014 Canada E100 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFNA 9 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0511604 215.0100 C014 NA 4 4 ng/g 0.2980000 NA 0.1430000 sd biological 4 4 0.3740000 NA 0.1810000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1430000 0.1810000 0.0511604
F003 Bhavsar_2014 2014 Canada E101 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFDA 10 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0511604 215.0100 C014 NA 4 4 ng/g 0.4230000 NA 0.1860000 sd biological 4 4 0.4930000 NA 0.2070000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1860000 0.2070000 0.0511604
F003 Bhavsar_2014 2014 Canada E102 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFUnDA 11 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0511604 215.0100 C014 NA 4 4 ng/g 0.5600000 NA 0.2510000 sd biological 4 4 0.6830000 NA 0.2860000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2510000 0.2860000 0.0511604
F003 Bhavsar_2014 2014 Canada E103 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFDoDA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0511604 215.0100 C014 NA 4 4 ng/g 0.1980000 NA 0.0950000 sd biological 4 4 0.2320000 NA 0.1030000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0950000 0.1030000 0.0511604
F003 Bhavsar_2014 2014 Canada E104 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFTrA 13 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0511604 215.0100 C014 NA 4 4 ng/g 0.4610000 NA 0.2170000 sd biological 4 4 0.5190000 NA 0.2120000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.2170000 0.2120000 0.0511604
F003 Bhavsar_2014 2014 Canada E105 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFTA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0511604 215.0100 C014 NA 4 4 ng/g 0.1280000 NA 0.0510000 sd biological 4 4 0.1290000 NA 0.0450000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0510000 0.0450000 0.0511604
F003 Bhavsar_2014 2014 Canada E106 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFHxS 6 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0511604 215.0100 C014 NA 4 4 ng/g 0.2580000 NA 0.0550000 sd biological 4 4 0.2450000 NA 0.0770000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0550000 0.0770000 0.0511604
F003 Bhavsar_2014 2014 Canada E107 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFOS 8 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0511604 215.0100 C014 NA 4 4 ng/g 18.1800000 NA 6.6860000 sd biological 4 4 21.6700000 NA 8.0080000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 6.6860000 8.0080000 0.0511604
F003 Bhavsar_2014 2014 Canada E108 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 PFDS 10 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0511604 215.0100 C014 NA 4 4 ng/g 0.4560000 NA 0.1770000 sd biological 4 4 0.5160000 NA 0.2440000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.1770000 0.2440000 0.0511604
F003 Bhavsar_2014 2014 Canada E109 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 6:6PFPIA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0511604 215.0100 C014 NA 4 4 ng/g 0.0020000 NA 0.0010000 sd biological 4 4 0.0020000 NA 0.0010000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0010000 0.0010000 0.0511604
F003 Bhavsar_2014 2014 Canada E110 Lake trout Salvelinus namaycush vertebrate freshwater fish 11.530000 6:8PFPIA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0511604 215.0100 C014 NA 4 4 ng/g 0.0170000 NA 0.0090000 sd biological 4 4 0.0160000 NA 0.0060000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0090000 0.0060000 0.0511604
F003 Bhavsar_2014 2014 Canada E111 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFNA 9 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0583152 188.6300 C014 NA 5 5 ng/g 0.0630000 NA 0.0210000 sd biological 5 5 0.0790000 NA 0.0230000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0210000 0.0230000 0.0583152
F003 Bhavsar_2014 2014 Canada E112 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFDA 10 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0583152 188.6300 C014 NA 5 5 ng/g 0.2480000 NA 0.0400000 sd biological 5 5 0.3490000 NA 0.0940000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0400000 0.0940000 0.0583152
F003 Bhavsar_2014 2014 Canada E113 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFUnDA 11 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0583152 188.6300 C014 NA 5 5 ng/g 0.2390000 NA 0.0400000 sd biological 5 5 0.3330000 NA 0.0910000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0400000 0.0910000 0.0583152
F003 Bhavsar_2014 2014 Canada E114 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFDoDA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0583152 188.6300 C014 NA 5 5 ng/g 0.1050000 NA 0.0190000 sd biological 5 5 0.1330000 NA 0.0120000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0190000 0.0120000 0.0583152
F003 Bhavsar_2014 2014 Canada E115 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFTrA 13 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0583152 188.6300 C014 NA 5 5 ng/g 0.1490000 NA 0.0200000 sd biological 5 5 0.1800000 NA 0.0210000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0200000 0.0210000 0.0583152
F003 Bhavsar_2014 2014 Canada E116 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFTA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0583152 188.6300 C014 NA 5 5 ng/g 0.0690000 NA 0.0090000 sd biological 5 5 0.0930000 NA 0.0230000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0090000 0.0230000 0.0583152
F003 Bhavsar_2014 2014 Canada E117 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFHxS 6 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0583152 188.6300 C014 NA 5 5 ng/g 0.0800000 NA 0.0250000 sd biological 5 5 0.0980000 NA 0.0340000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0250000 0.0340000 0.0583152
F003 Bhavsar_2014 2014 Canada E118 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFOS 8 NA Yes Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0583152 188.6300 C014 NA 5 5 ng/g 36.7900000 NA 1.6240000 sd biological 5 5 45.0900000 NA 3.7090000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 1.6240000 3.7090000 0.0583152
F003 Bhavsar_2014 2014 Canada E119 Walleye Sander vitreus vertebrate freshwater fish 18.710000 PFDS 10 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0583152 188.6300 C014 NA 5 5 ng/g 0.1060000 NA 0.0240000 sd biological 5 5 0.1780000 NA 0.0940000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0240000 0.0940000 0.0583152
F003 Bhavsar_2014 2014 Canada E120 Walleye Sander vitreus vertebrate freshwater fish 18.710000 6:6PFPIA 12 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0583152 188.6300 C014 NA 5 5 ng/g 0.0260000 NA 0.0060000 sd biological 5 5 0.0350000 NA 0.0060000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0060000 0.0060000 0.0583152
F003 Bhavsar_2014 2014 Canada E121 Walleye Sander vitreus vertebrate freshwater fish 18.710000 6:8PFPIA 14 NA No Baking oil-based NA 200 900 No Yes canola oil 11.000 11.000 0.0583152 188.6300 C014 NA 5 5 ng/g 0.0670000 NA 0.0100000 sd biological 5 5 0.0630000 NA 0.0170000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0100000 0.0170000 0.0583152
F003 Bhavsar_2014 2014 Canada E122 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFNA 9 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.506 0.506 0.0028457 177.8100 C015 NA 5 5 ng/g 0.0630000 NA 0.0210000 sd biological 5 5 0.0740000 NA 0.0140000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0210000 0.0140000 0.0028457
F003 Bhavsar_2014 2014 Canada E123 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFDA 10 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.506 0.506 0.0028457 177.8100 C015 NA 5 5 ng/g 0.2480000 NA 0.0400000 sd biological 5 5 0.3380000 NA 0.0980000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0400000 0.0980000 0.0028457
F003 Bhavsar_2014 2014 Canada E124 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFUnDA 11 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.506 0.506 0.0028457 177.8100 C015 NA 5 5 ng/g 0.2390000 NA 0.0400000 sd biological 5 5 0.3480000 NA 0.1020000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0400000 0.1020000 0.0028457
F003 Bhavsar_2014 2014 Canada E125 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFDoDA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.506 0.506 0.0028457 177.8100 C015 NA 5 5 ng/g 0.1050000 NA 0.0190000 sd biological 5 5 0.1440000 NA 0.0370000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0190000 0.0370000 0.0028457
F003 Bhavsar_2014 2014 Canada E126 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFTrA 13 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.506 0.506 0.0028457 177.8100 C015 NA 5 5 ng/g 0.1490000 NA 0.0200000 sd biological 5 5 0.2170000 NA 0.0410000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0200000 0.0410000 0.0028457
F003 Bhavsar_2014 2014 Canada E127 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFTA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.506 0.506 0.0028457 177.8100 C015 NA 5 5 ng/g 0.0690000 NA 0.0090000 sd biological 5 5 0.0940000 NA 0.0250000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0090000 0.0250000 0.0028457
F003 Bhavsar_2014 2014 Canada E128 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFHxS 6 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.506 0.506 0.0028457 177.8100 C015 NA 5 5 ng/g 0.0800000 NA 0.0250000 sd biological 5 5 0.0880000 NA 0.0360000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0250000 0.0360000 0.0028457
F003 Bhavsar_2014 2014 Canada E129 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFOS 8 NA Yes Broiling oil-based NA 300 600 No Yes canola oil 0.506 0.506 0.0028457 177.8100 C015 NA 5 5 ng/g 36.7900000 NA 1.6240000 sd biological 5 5 52.6900000 NA 14.6200000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 1.6240000 14.6200000 0.0028457
F003 Bhavsar_2014 2014 Canada E130 Walleye Sander vitreus vertebrate freshwater fish 24.090000 PFDS 10 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.506 0.506 0.0028457 177.8100 C015 NA 5 5 ng/g 0.1060000 NA 0.0240000 sd biological 5 5 0.1890000 NA 0.0800000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0240000 0.0800000 0.0028457
F003 Bhavsar_2014 2014 Canada E131 Walleye Sander vitreus vertebrate freshwater fish 24.090000 6:6PFPIA 12 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.506 0.506 0.0028457 177.8100 C015 NA 5 5 ng/g 0.0260000 NA 0.0060000 sd biological 5 5 0.0400000 NA 0.0080000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0060000 0.0080000 0.0028457
F003 Bhavsar_2014 2014 Canada E132 Walleye Sander vitreus vertebrate freshwater fish 24.090000 6:8PFPIA 14 NA No Broiling oil-based NA 300 600 No Yes canola oil 0.506 0.506 0.0028457 177.8100 C015 NA 5 5 ng/g 0.0670000 NA 0.0100000 sd biological 5 5 0.0870000 NA 0.0120000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0100000 0.0120000 0.0028457
F003 Bhavsar_2014 2014 Canada E133 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFNA 9 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0615832 178.6200 C016 NA 5 5 ng/g 0.0630000 NA 0.0210000 sd biological 5 5 0.0670000 NA 0.0150000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0210000 0.0150000 0.0615832
F003 Bhavsar_2014 2014 Canada E134 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFDA 10 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0615832 178.6200 C016 NA 5 5 ng/g 0.2480000 NA 0.0400000 sd biological 5 5 0.2990000 NA 0.0720000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0400000 0.0720000 0.0615832
F003 Bhavsar_2014 2014 Canada E135 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFUnDA 11 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0615832 178.6200 C016 NA 5 5 ng/g 0.2390000 NA 0.0400000 sd biological 5 5 0.3070000 NA 0.0760000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0400000 0.0760000 0.0615832
F003 Bhavsar_2014 2014 Canada E136 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFDoDA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0615832 178.6200 C016 NA 5 5 ng/g 0.1050000 NA 0.0190000 sd biological 5 5 0.1290000 NA 0.0490000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0190000 0.0490000 0.0615832
F003 Bhavsar_2014 2014 Canada E137 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFTrA 13 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0615832 178.6200 C016 NA 5 5 ng/g 0.1490000 NA 0.0200000 sd biological 5 5 0.1790000 NA 0.0540000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0200000 0.0540000 0.0615832
F003 Bhavsar_2014 2014 Canada E138 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFTA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0615832 178.6200 C016 NA 5 5 ng/g 0.0690000 NA 0.0090000 sd biological 5 5 0.0870000 NA 0.0340000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0090000 0.0340000 0.0615832
F003 Bhavsar_2014 2014 Canada E139 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFHxS 6 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0615832 178.6200 C016 NA 5 5 ng/g 0.0800000 NA 0.0250000 sd biological 5 5 0.0830000 NA 0.0270000 biological NA ng/g Probably <0.006 Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0250000 0.0270000 0.0615832
F003 Bhavsar_2014 2014 Canada E140 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFOS 8 NA Yes Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0615832 178.6200 C016 NA 5 5 ng/g 36.7900000 NA 1.6240000 sd biological 5 5 44.5100000 NA 7.7180000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 1.6240000 7.7180000 0.0615832
F003 Bhavsar_2014 2014 Canada E141 Walleye Sander vitreus vertebrate freshwater fish 14.450000 PFDS 10 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0615832 178.6200 C016 NA 5 5 ng/g 0.1060000 NA 0.0240000 sd biological 5 5 0.1570000 NA 0.0660000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0240000 0.0660000 0.0615832
F003 Bhavsar_2014 2014 Canada E142 Walleye Sander vitreus vertebrate freshwater fish 14.450000 6:6PFPIA 12 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0615832 178.6200 C016 NA 5 5 ng/g 0.0260000 NA 0.0060000 sd biological 5 5 0.0290000 NA 0.0040000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0060000 0.0040000 0.0615832
F003 Bhavsar_2014 2014 Canada E143 Walleye Sander vitreus vertebrate freshwater fish 14.450000 6:8PFPIA 14 NA No Frying oil-based NA 175 600 No Yes canola oil 11.000 11.000 0.0615832 178.6200 C016 NA 5 5 ng/g 0.0670000 NA 0.0100000 sd biological 5 5 0.0770000 NA 0.0050000 biological NA ng/g Not provided Not provided Shared control Table S3 No We assumed that all specimens of one species were analysed separately and that NO pooling occured. However, we can not be fully sure. We also assumed that the provided sd was biological. NA 0.0100000 0.0050000 0.0615832
F005 DelGobbo_2008 2008 Canada E144 Catfish Ictalurus punctatus vertebrate freshwater fish NA PFOS 8 linear Yes Frying oil-based NA 163 900 No Yes sesame oil NA NA 0.0625000 NA C017 NA 19 1 ng/g 1.5657252 NA NA Not available because sample size is one. technical 19 1 0.8987374 NA NA technical 4 ng/g 0.3646058391 1.093817517 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species ML NA NA 0.0625000
F005 DelGobbo_2008 2008 Canada E145 Grouper Epinephelus itajara vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based NA 163 900 No Yes sesame oil NA NA 0.0625000 NA C018 NA 14 1 ng/g 1.3600000 NA NA Not available because sample size is one. technical 14 1 0.0169896 LOD NA technical 4 ng/g 0.01698962618 0.05096887855 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 0.0625000
F005 DelGobbo_2008 2008 Canada E146 Grouper Epinephelus itajara vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based NA 163 900 No Yes sesame oil NA NA 0.0625000 NA C018 NA 14 1 ng/g 0.3715856 LOD NA Not available because sample size is one. technical 14 1 0.4700000 NA NA technical 4 ng/g 0.3715856481 1.114756944 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 0.0625000
F005 DelGobbo_2008 2008 Canada E147 Monkfish Lophius americanus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C019 NA 9 1 ng/g 0.0774969 LOD NA Not available because sample size is one. technical 9 1 0.0600000 NA NA technical 4 ng/g 0.07749693852 0.2324908155 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E148 Monkfish Lophius americanus vertebrate marine fish NA PFNA 9 NA Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C019 NA 9 1 ng/g 1.3400000 NA NA Not available because sample size is one. technical 9 1 0.0032120 LOD NA technical 4 ng/g 0.0032120281 0.009636084301 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E149 Monkfish Lophius americanus vertebrate marine fish NA PFUnDA 11 NA Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C019 NA 9 1 ng/g 0.0270203 LOD NA Not available because sample size is one. technical 9 1 0.3900000 NA NA technical 4 ng/g 0.02702032357 0.08106097072 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E150 Monkfish Lophius americanus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C019 NA 9 1 ng/g 1.3400000 NA NA Not available because sample size is one. technical 9 1 0.2200000 NA NA technical 4 ng/g 0.2333732266 0.7001196799 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E151 Octopus Bathypolypus arcticus invertebrate mollusca NA PFOA 8 NA Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C020 NA 15 1 ng/g 0.7800000 NA NA Not available because sample size is one. technical 15 1 0.0600000 NA NA technical 3 ng/g 0.02612585327 0.0783775598 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E152 Octopus Bathypolypus arcticus invertebrate mollusca NA PFNA 9 NA Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C020 NA 15 1 ng/g 1.2900000 NA NA Not available because sample size is one. technical 15 1 0.0261259 LOD NA technical 3 ng/g 0.02612585327 0.0783775598 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E153 Octopus Bathypolypus arcticus invertebrate mollusca NA PFDA 10 NA No Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C020 NA 15 1 ng/g 1.5500000 NA NA Not available because sample size is one. technical 15 1 0.0120876 LOD NA technical 3 ng/g 0.01208759187 0.03626277562 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E154 Octopus Bathypolypus arcticus invertebrate mollusca NA PFUnDA 11 NA Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C020 NA 15 1 ng/g 1.8800000 NA NA Not available because sample size is one. technical 15 1 1.5900000 NA NA technical 3 ng/g 0.02340346342 0.07021039026 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E155 Octopus Bathypolypus arcticus invertebrate mollusca NA PFTA 14 NA No Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C020 NA 15 1 ng/g 2.6100000 NA NA Not available because sample size is one. technical 15 1 0.0071943 LOD NA technical 3 ng/g 0.007194278092 0.02158283428 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E156 Octopus Bathypolypus arcticus invertebrate mollusca NA PFOS 8 linear Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C020 NA 15 1 ng/g 0.5086163 LOD NA Not available because sample size is one. technical 15 1 0.2300000 NA NA technical 3 ng/g 0.5086163051 1.525848915 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E157 Red snapper Lutjanus campechanus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C021 NA 19 1 ng/g 1.4600000 NA NA Not available because sample size is one. technical 19 1 0.2100000 NA NA technical 4 ng/g 0.335745729 1.007237187 Shared control Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E158 Red snapper Lutjanus campechanus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based NA 163 900 No Yes sesame oil NA NA 0.0625000 NA C021 NA 19 1 ng/g 1.4600000 NA NA Not available because sample size is one. technical 19 1 0.7800000 NA NA technical 4 ng/g 0.2127077334 0.6381232001 Shared control Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 0.0625000
F005 DelGobbo_2008 2008 Canada E159 Sea squirt Diplosoma listerianum vertebrate tunicata NA PFOA 8 NA Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C022 NA 22 1 ng/g 1.5800000 NA NA Not available because sample size is one. technical 22 1 1.5900000 NA NA technical 3 ng/g 0.03079926295 0.09239778884 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E160 Sea squirt Diplosoma listerianum vertebrate tunicata NA PFNA 9 NA Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C022 NA 22 1 ng/g 1.3200000 NA NA Not available because sample size is one. technical 22 1 0.9600000 NA NA technical 3 ng/g 0.004661629686 0.01398488906 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E161 Skate Amblyraja hyperborea vertebrate tunicata NA PFNA 9 NA Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C023 NA 14 1 ng/g 1.0900000 NA NA Not available because sample size is one. technical 14 1 0.0027709 LOD NA technical 4 ng/g 0.002770915071 0.008312745212 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E162 Skate Amblyraja hyperborea vertebrate tunicata NA PFUnDA 11 NA Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C023 NA 14 1 ng/g 1.5500000 NA NA Not available because sample size is one. technical 14 1 1.3500000 NA NA technical 4 ng/g 0.01203365344 0.03610096033 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E163 Skate Amblyraja hyperborea vertebrate tunicata NA PFDoDA 12 NA No Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C023 NA 14 1 ng/g 1.3300000 NA NA Not available because sample size is one. technical 14 1 0.0255728 LOD NA technical 4 ng/g 0.02557281543 0.07671844628 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E164 Skate Amblyraja hyperborea vertebrate tunicata NA PFTA 14 NA No Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C023 NA 14 1 ng/g 0.6700000 NA NA Not available because sample size is one. technical 14 1 0.0070174 LOD NA technical 4 ng/g 0.007017439682 0.02105231905 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E165 Skate Amblyraja hyperborea vertebrate tunicata NA PFOS 8 linear Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C023 NA 14 1 ng/g 1.5100000 NA NA Not available because sample size is one. technical 14 1 0.8800000 NA NA technical 4 ng/g 0.3642166626 1.092649988 Dependent Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E166 Yellow croaker Larimichthys polyactis vertebrate tunicata NA PFUnDA 11 NA Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C024 NA 35 1 ng/g 1.5700000 NA NA Not available because sample size is one. technical 35 1 0.0179042 LOD NA technical 4 ng/g 0.0179042065 0.0537126195 Shared control Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E167 Yellow croaker Larimichthys polyactis vertebrate tunicata NA PFOS 8 linear Yes Boiling water-based NA 100 420 Yes No NA NA NA 30.0000000 NA C024 NA 35 1 ng/g 1.6800000 NA NA Not available because sample size is one. technical 35 1 0.8900000 NA NA technical 4 ng/g 0.3768854178 1.130656253 Shared control Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 30.0000000
F005 DelGobbo_2008 2008 Canada E168 Yellow croaker Larimichthys polyactis vertebrate tunicata NA PFUnDA 11 NA Yes Frying oil-based NA 163 900 No Yes sesame oil NA NA 0.0625000 NA C025 NA 35 1 ng/g 1.5700000 NA NA Not available because sample size is one. technical 35 1 2.1100000 NA NA technical 4 ng/g 0.0165860278 0.04975808341 Shared control Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 0.0625000
F005 DelGobbo_2008 2008 Canada E169 Yellow croaker Larimichthys polyactis vertebrate tunicata NA PFOS 8 linear Yes Frying oil-based NA 163 900 No Yes sesame oil NA NA 0.0625000 NA C025 NA 35 1 ng/g 1.6800000 NA NA Not available because sample size is one. technical 35 1 0.6800000 NA NA technical 4 ng/g 0.3921755285 1.176526586 Shared control Table 3 Yes Authors replied; scientific names of seafood species were not provided, therefore we assumed the most common local species NA NA NA 0.0625000
F006 Hu_2020 2020 China E170 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFBA 3 NA No Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA NA NA 70.0000 C026 NA 5 5 ng/g 6.9619423 NA 7.4193907 sd biological 5 5 5.3412073 NA 1.6889253 biological NA ng/g Not provided 12.2 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. ML 7.4193907 1.6889253 NA
F006 Hu_2020 2020 China E171 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFOA 8 NA Yes Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA NA NA 70.0000 C026 NA 5 5 ng/g 0.2098410 NA 0.1560332 sd biological 5 5 0.2674068 NA 0.0800584 biological NA ng/g Not provided 0.226 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.1560332 0.0800584 NA
F006 Hu_2020 2020 China E172 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFBS 4 NA Yes Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA NA NA 70.0000 C026 NA 5 5 ng/g 24.8753463 NA 23.9889753 sd biological 5 5 23.9801208 NA 26.8453690 biological NA ng/g Not provided 1.01 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 23.9889753 26.8453690 NA
F006 Hu_2020 2020 China E173 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFOS 8 NA Yes Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA NA NA 70.0000 C026 NA 5 5 ng/g 86.6890380 NA 39.4592027 sd biological 5 5 122.4133110 NA 62.4690572 biological NA ng/g Not provided 1.57 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 39.4592027 62.4690572 NA
F006 Hu_2020 2020 China E174 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFHpA 7 NA No Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA NA NA 70.0000 C026 NA 5 5 ng/g 24.2980562 NA 30.6129835 sd biological 5 5 55.3995680 NA 55.3995680 biological NA ng/g Not provided 0.47 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 30.6129835 55.3995680 NA
F006 Hu_2020 2020 China E175 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFDoDA 12 NA No Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA NA NA 70.0000 C026 NA 5 5 ng/g 1.5680310 NA 0.5599538 sd biological 5 5 2.2676991 NA 1.5334164 biological NA ng/g Not provided 0.093 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.5599538 1.5334164 NA
F006 Hu_2020 2020 China E176 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 PFHxS 6 NA Yes Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA NA NA 70.0000 C026 NA 5 5 ng/g 1.8092949 NA 2.3827419 sd biological 5 5 0.8685897 NA 0.3034431 biological NA ng/g Not provided 0.155 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 2.3827419 0.3034431 NA
F006 Hu_2020 2020 China E177 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 14.380000 FOSA 8 NA Yes Steaming water-based on a stainless-steel plate in a steamer 100 480 Yes No NA NA NA NA 70.0000 C026 NA 5 5 ng/g 2.5990437 NA 1.6889253 sd biological 5 5 2.3838798 NA 1.2904183 biological NA ng/g Not provided 0.026 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 1.6889253 1.2904183 NA
F006 Hu_2020 2020 China E178 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFBA 3 NA No Boiling water-based NA 100 120 Yes No NA 300.000 300.000 4.2857143 70.0000 C027 NA 5 5 ng/g 6.9619423 NA 7.4193907 sd biological 5 5 4.9146982 NA 7.4344664 biological NA ng/g Not provided 12.2 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 7.4193907 7.4344664 4.2857143
F006 Hu_2020 2020 China E179 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFOA 8 NA Yes Boiling water-based NA 100 120 Yes No NA 300.000 300.000 4.2857143 70.0000 C027 NA 5 5 ng/g 0.2098410 NA 0.1560332 sd biological 5 5 0.1932566 NA 0.0707998 biological NA ng/g Not provided 0.226 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.1560332 0.0707998 4.2857143
F006 Hu_2020 2020 China E180 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFBS 4 NA Yes Boiling water-based NA 100 120 Yes No NA 300.000 300.000 4.2857143 70.0000 C027 NA 5 5 ng/g 24.8753463 NA 23.9889753 sd biological 5 5 10.8230680 NA 7.4606797 biological NA ng/g Not provided 1.01 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 23.9889753 7.4606797 4.2857143
F006 Hu_2020 2020 China E181 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFOS 8 NA Yes Boiling water-based NA 100 120 Yes No NA 300.000 300.000 4.2857143 70.0000 C027 NA 5 5 ng/g 86.6890380 NA 39.4592027 sd biological 5 5 97.7348993 NA 23.1725546 biological NA ng/g Not provided 1.57 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 39.4592027 23.1725546 4.2857143
F006 Hu_2020 2020 China E182 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFHpA 7 NA No Boiling water-based NA 100 120 Yes No NA 300.000 300.000 4.2857143 70.0000 C027 NA 5 5 ng/g 24.2980562 NA 30.6129835 sd biological 5 5 13.7149028 NA 23.6036055 biological NA ng/g Not provided 0.47 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 30.6129835 23.6036055 4.2857143
F006 Hu_2020 2020 China E183 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFDoDA 12 NA No Boiling water-based NA 100 120 Yes No NA 300.000 300.000 4.2857143 70.0000 C027 NA 5 5 ng/g 1.5680310 NA 0.5599538 sd biological 5 5 2.3534292 NA 2.4839931 biological NA ng/g Not provided 0.093 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.5599538 2.4839931 4.2857143
F006 Hu_2020 2020 China E184 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 PFHxS 6 NA Yes Boiling water-based NA 100 120 Yes No NA 300.000 300.000 4.2857143 70.0000 C027 NA 5 5 ng/g 1.8092949 NA 2.3827419 sd biological 5 5 0.6506410 NA 0.1079317 biological NA ng/g Not provided 0.155 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 2.3827419 0.1079317 4.2857143
F006 Hu_2020 2020 China E185 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 16.480000 FOSA 8 NA Yes Boiling water-based NA 100 120 Yes No NA 300.000 300.000 4.2857143 70.0000 C027 NA 5 5 ng/g 2.5990437 NA 1.6889253 sd biological 5 5 2.2540984 NA 1.2484167 biological NA ng/g Not provided 0.026 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 1.6889253 1.2484167 4.2857143
F006 Hu_2020 2020 China E186 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFBA 3 NA No Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100.000 100.000 1.4285714 70.0000 C028 NA 5 5 ng/g 6.9619423 NA 7.4193907 sd biological 5 5 7.9068241 NA 9.3812679 biological NA ng/g Not provided 12.2 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 7.4193907 9.3812679 1.4285714
F006 Hu_2020 2020 China E187 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFOA 8 NA Yes Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100.000 100.000 1.4285714 70.0000 C028 NA 5 5 ng/g 0.2098410 NA 0.1560332 sd biological 5 5 0.2308114 NA 0.1541468 biological NA ng/g Not provided 0.226 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.1560332 0.1541468 1.4285714
F006 Hu_2020 2020 China E188 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFBS 4 NA Yes Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100.000 100.000 1.4285714 70.0000 C028 NA 5 5 ng/g 24.8753463 NA 23.9889753 sd biological 5 5 9.8657220 NA 5.8014926 biological NA ng/g Not provided 1.01 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 23.9889753 5.8014926 1.4285714
F006 Hu_2020 2020 China E189 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFOS 8 NA Yes Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100.000 100.000 1.4285714 70.0000 C028 NA 5 5 ng/g 86.6890380 NA 39.4592027 sd biological 5 5 134.4379195 NA 58.0538019 biological NA ng/g Not provided 1.57 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 39.4592027 58.0538019 1.4285714
F006 Hu_2020 2020 China E190 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFHpA 7 NA No Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100.000 100.000 1.4285714 70.0000 C028 NA 5 5 ng/g 24.2980562 NA 30.6129835 sd biological 5 5 23.7041037 NA 35.9297367 biological NA ng/g Not provided 0.47 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 30.6129835 35.9297367 1.4285714
F006 Hu_2020 2020 China E191 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFDoDA 12 NA No Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100.000 100.000 1.4285714 70.0000 C028 NA 5 5 ng/g 1.5680310 NA 0.5599538 sd biological 5 5 2.8733407 NA 2.7470061 biological NA ng/g Not provided 0.093 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.5599538 2.7470061 1.4285714
F006 Hu_2020 2020 China E192 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 PFHxS 6 NA Yes Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100.000 100.000 1.4285714 70.0000 C028 NA 5 5 ng/g 1.8092949 NA 2.3827419 sd biological 5 5 1.1602564 NA 0.7375647 biological NA ng/g Not provided 0.155 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 2.3827419 0.7375647 1.4285714
F006 Hu_2020 2020 China E193 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 23.140000 FOSA 8 NA Yes Frying oil-based In a stainless-steel pan 180 150 No Yes Not specified 100.000 100.000 1.4285714 70.0000 C028 NA 5 5 ng/g 2.5990437 NA 1.6889253 sd biological 5 5 3.7500000 NA 3.7411362 biological NA ng/g Not provided 0.026 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 1.6889253 3.7411362 1.4285714
F006 Hu_2020 2020 China E194 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFBA 3 NA No Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10.000 10.000 0.1428571 70.0000 C029 NA 5 5 ng/g 6.9619423 NA 7.4193907 sd biological 5 5 4.8490814 NA 6.9303363 biological NA ng/g Not provided 12.2 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 7.4193907 6.9303363 0.1428571
F006 Hu_2020 2020 China E195 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFOA 8 NA Yes Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10.000 10.000 0.1428571 70.0000 C029 NA 5 5 ng/g 0.2098410 NA 0.1560332 sd biological 5 5 0.1652961 NA 0.0630496 biological NA ng/g Not provided 0.226 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.1560332 0.0630496 0.1428571
F006 Hu_2020 2020 China E196 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFBS 4 NA Yes Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10.000 10.000 0.1428571 70.0000 C029 NA 5 5 ng/g 24.8753463 NA 23.9889753 sd biological 5 5 7.5376305 NA 1.5022632 biological NA ng/g Not provided 1.01 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 23.9889753 1.5022632 0.1428571
F006 Hu_2020 2020 China E197 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFOS 8 NA Yes Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10.000 10.000 0.1428571 70.0000 C029 NA 5 5 ng/g 86.6890380 NA 39.4592027 sd biological 5 5 121.7142058 NA 62.5574247 biological NA ng/g Not provided 1.57 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 39.4592027 62.5574247 0.1428571
F006 Hu_2020 2020 China E198 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFHpA 7 NA No Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10.000 10.000 0.1428571 70.0000 C029 NA 5 5 ng/g 24.2980562 NA 30.6129835 sd biological 5 5 10.0971922 NA 16.4902451 biological NA ng/g Not provided 0.47 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 30.6129835 16.4902451 0.1428571
F006 Hu_2020 2020 China E199 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFDoDA 12 NA No Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10.000 10.000 0.1428571 70.0000 C029 NA 5 5 ng/g 1.5680310 NA 0.5599538 sd biological 5 5 2.9120575 NA 3.3602781 biological NA ng/g Not provided 0.093 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 0.5599538 3.3602781 0.1428571
F006 Hu_2020 2020 China E200 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 PFHxS 6 NA Yes Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10.000 10.000 0.1428571 70.0000 C029 NA 5 5 ng/g 1.8092949 NA 2.3827419 sd biological 5 5 0.8253205 NA 0.2542197 biological NA ng/g Not provided 0.155 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 2.3827419 0.2542197 0.1428571
F006 Hu_2020 2020 China E201 Grass carp Ctenopharyngodon idell vertebrate freshwater fish 21.310000 FOSA 8 NA Yes Grilling oil-based Domestic electric oven set to broil 210 600 No Yes Not specified 10.000 10.000 0.1428571 70.0000 C029 NA 5 5 ng/g 2.5990437 NA 1.6889253 sd biological 5 5 2.2814208 NA 0.4304018 biological NA ng/g Not provided 0.026 Shared control Figure 3 No We used meta-digitize to get effect sizes, and used boxplot-option to calculate sd, also we assumed that individual fish were not pooled, but each fish was measured individually, we also assumed that the sd provided was biological. NA 1.6889253 0.4304018 0.1428571
F007 Kim_2020 2020 Korea E202 Mackerel Scomber japonicus vertebrate marine fish NA PFOA 8 NA Yes Grilling oil-based NA NA 360 No Yes Not specified 5.000 5.000 0.0500000 100.0000 C030 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. For volume of cooking liquid: 1 cup is 250 ml, accordingly for table spoon etc. ML NA NA 0.0500000
F007 Kim_2020 2020 Korea E203 Mackerel Scomber japonicus vertebrate marine fish NA PFOA 8 NA Yes Braising water-based NA 100 1500 Yes No NA 250.000 250.000 2.5000000 100.0000 C031 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.1100000 NA 0.0000000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 2.5000000
F007 Kim_2020 2020 Korea E204 Mackerel Scomber japonicus vertebrate marine fish NA PFOA 8 NA Yes Steaming water-based NA 100 900 Yes No NA 250.000 250.000 2.5000000 100.0000 C032 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0200000 NA NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 2.5000000
F007 Kim_2020 2020 Korea E205 Mackerel Scomber japonicus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based NA 160 300 No Yes Not specified 750.000 750.000 7.5000000 100.0000 C033 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0900000 NA 0.0600000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 7.5000000
F007 Kim_2020 2020 Korea E206 Mackerel Scomber japonicus vertebrate marine fish NA PFBA 3 NA No Grilling oil-based NA NA 360 No Yes Not specified 5.000 5.000 0.0500000 100.0000 C030 NA 10 1 ng/g 0.1600000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 0.0500000
F007 Kim_2020 2020 Korea E207 Mackerel Scomber japonicus vertebrate marine fish NA PFBA 3 NA No Braising water-based NA 100 1500 Yes No NA 250.000 250.000 2.5000000 100.0000 C031 NA 10 1 ng/g 0.1600000 NA 0.0100000 sd technical 10 1 0.1300000 NA 0.0400000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 2.5000000
F007 Kim_2020 2020 Korea E208 Mackerel Scomber japonicus vertebrate marine fish NA PFBA 3 NA No Steaming water-based NA 100 900 Yes No NA 250.000 250.000 2.5000000 100.0000 C032 NA 10 1 ng/g 0.1600000 NA 0.0100000 sd technical 10 1 0.1400000 NA 0.0100000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 2.5000000
F007 Kim_2020 2020 Korea E209 Mackerel Scomber japonicus vertebrate marine fish NA PFBA 3 NA No Frying oil-based NA 160 300 No Yes Not specified 750.000 750.000 7.5000000 100.0000 C033 NA 10 1 ng/g 0.1600000 NA 0.0100000 sd technical 10 1 0.0900000 NA 0.0000000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 7.5000000
F007 Kim_2020 2020 Korea E210 Mackerel Scomber japonicus vertebrate marine fish NA PFHpA 7 NA No Grilling oil-based NA NA 360 No Yes Not specified 5.000 5.000 0.0500000 100.0000 C030 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 0.0500000
F007 Kim_2020 2020 Korea E211 Mackerel Scomber japonicus vertebrate marine fish NA PFHpA 7 NA No Braising water-based NA 100 1500 Yes No NA 250.000 250.000 2.5000000 100.0000 C031 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 2.5000000
F007 Kim_2020 2020 Korea E212 Mackerel Scomber japonicus vertebrate marine fish NA PFHpA 7 NA No Steaming water-based NA 100 900 Yes No NA 250.000 250.000 2.5000000 100.0000 C032 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 2.5000000
F007 Kim_2020 2020 Korea E213 Mackerel Scomber japonicus vertebrate marine fish NA PFHpA 7 NA No Frying oil-based NA 160 300 No Yes Not specified 750.000 750.000 7.5000000 100.0000 C033 NA 10 1 ng/g 0.0700000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 7.5000000
F007 Kim_2020 2020 Korea E214 Mackerel Scomber japonicus vertebrate marine fish NA PFDoDA 12 NA No Grilling oil-based NA NA 360 No Yes Not specified 5.000 5.000 0.0500000 100.0000 C030 NA 10 1 ng/g 0.0200000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 0.0500000
F007 Kim_2020 2020 Korea E215 Mackerel Scomber japonicus vertebrate marine fish NA PFDoDA 12 NA No Braising water-based NA 100 1500 Yes No NA 250.000 250.000 2.5000000 100.0000 C031 NA 10 1 ng/g 0.0200000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 2.5000000
F007 Kim_2020 2020 Korea E216 Mackerel Scomber japonicus vertebrate marine fish NA PFDoDA 12 NA No Steaming water-based NA 100 900 Yes No NA 250.000 250.000 2.5000000 100.0000 C032 NA 10 1 ng/g 0.0200000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 2.5000000
F007 Kim_2020 2020 Korea E217 Mackerel Scomber japonicus vertebrate marine fish NA PFDoDA 12 NA No Frying oil-based NA 160 300 No Yes Not specified 750.000 750.000 7.5000000 100.0000 C033 NA 10 1 ng/g 0.0200000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 7.5000000
F007 Kim_2020 2020 Korea E218 Mackerel Scomber japonicus vertebrate marine fish NA PFTrA 13 NA No Grilling oil-based NA NA 360 No Yes Not specified 5.000 5.000 0.0500000 100.0000 C030 NA 10 1 ng/g 0.0800000 NA 0.0100000 sd technical 10 1 0.0500000 NA 0.0000000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 0.0500000
F007 Kim_2020 2020 Korea E219 Mackerel Scomber japonicus vertebrate marine fish NA PFTrA 13 NA No Braising water-based NA 100 1500 Yes No NA 250.000 250.000 2.5000000 100.0000 C031 NA 10 1 ng/g 0.0800000 NA 0.0100000 sd technical 10 1 0.0600000 NA 0.0200000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 2.5000000
F007 Kim_2020 2020 Korea E220 Mackerel Scomber japonicus vertebrate marine fish NA PFTrA 13 NA No Steaming water-based NA 100 900 Yes No NA 250.000 250.000 2.5000000 100.0000 C032 NA 10 1 ng/g 0.0800000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 2.5000000
F007 Kim_2020 2020 Korea E221 Mackerel Scomber japonicus vertebrate marine fish NA PFTrA 13 NA No Frying oil-based NA 160 300 No Yes Not specified 750.000 750.000 7.5000000 100.0000 C033 NA 10 1 ng/g 0.0800000 NA 0.0100000 sd technical 10 1 0.0600000 NA 0.0000000 technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 7.5000000
F007 Kim_2020 2020 Korea E222 Mackerel Scomber japonicus vertebrate marine fish NA PFBS 4 NA Yes Grilling oil-based NA NA 360 No Yes Not specified 5.000 5.000 0.0500000 100.0000 C030 NA 10 1 ng/g 0.1900000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 0.0500000
F007 Kim_2020 2020 Korea E223 Mackerel Scomber japonicus vertebrate marine fish NA PFBS 4 NA Yes Braising water-based NA 100 1500 Yes No NA 250.000 250.000 2.5000000 100.0000 C031 NA 10 1 ng/g 0.1900000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 2.5000000
F007 Kim_2020 2020 Korea E224 Mackerel Scomber japonicus vertebrate marine fish NA PFBS 4 NA Yes Steaming water-based NA 100 900 Yes No NA 250.000 250.000 2.5000000 100.0000 C032 NA 10 1 ng/g 0.1900000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 2.5000000
F007 Kim_2020 2020 Korea E225 Mackerel Scomber japonicus vertebrate marine fish NA PFBS 4 NA Yes Frying oil-based NA 160 300 No Yes Not specified 750.000 750.000 7.5000000 100.0000 C033 NA 10 1 ng/g 0.1900000 NA 0.0100000 sd technical 10 1 0.0200000 LOD NA technical NA ng/g 0.02 to 0.09 0.08 to 0.27 Shared control Table 2 No We assumed the lowest value (0.02) for LOD. Weight of fish sample per batch: We follow the cooking instructions of table 1. We assume one piece of fish was used per batched. NA NA NA 7.5000000
F008 Luo_2019 2019 Korea E316 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFOA 8 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500.000 2500.000 2.5000000 1000.0000 C040 NA 5 1 ng/g 20.7900000 NA 0.1700000 sd technical 5 1 16.7700000 NA 0.4200000 technical NA ng/g 0.06 0.19 Dependent Table 4 No Scientific name of swimming crab not provided in paper, inferred as this species of swimming crab is commonly eaten in South korea (Kim, S., Lee, M.J., Lee, J.J., Choi, S.H. and Kim, B.S., 2017. Analysis of microbiota of the swimming crab (Portunus trituberculatus) in South Korea to identify risk markers for foodborne illness. LWT, 86, pp.483-491.) NA NA NA 2.5000000
F008 Luo_2019 2019 Korea E317 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFOS 8 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500.000 2500.000 2.5000000 1000.0000 C040 NA 5 1 ng/g 0.8100000 NA 0.0200000 sd technical 5 1 0.7400000 NA 0.0300000 technical NA ng/g 0.07 0.07 Dependent Table 4 No NA NA NA NA 2.5000000
F008 Luo_2019 2019 Korea E318 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFBA 3 NA No Boiling water-based Boiled with radish 100 600 Yes No NA 2500.000 2500.000 2.5000000 1000.0000 C040 NA 5 1 ng/g 0.1400000 NA 0.0100000 sd technical 5 1 0.0400000 NA 0.0100000 technical NA ng/g 0.06 0.19 Dependent Table 4 No NA NA NA NA 2.5000000
F008 Luo_2019 2019 Korea E319 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFHpA 7 NA No Boiling water-based Boiled with radish 100 600 Yes No NA 2500.000 2500.000 2.5000000 1000.0000 C040 NA 5 1 ng/g 0.3700000 NA 0.0300000 sd technical 5 1 0.3200000 NA 0.0100000 technical NA ng/g 0.06 0.17 Dependent Table 4 No NA NA NA NA 2.5000000
F008 Luo_2019 2019 Korea E320 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFNA 9 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500.000 2500.000 2.5000000 1000.0000 C040 NA 5 1 ng/g 2.8900000 NA 0.0200000 sd technical 5 1 2.3000000 NA 0.0300000 technical NA ng/g 0.03 0.08 Dependent Table 4 No NA NA NA NA 2.5000000
F008 Luo_2019 2019 Korea E321 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFDA 10 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500.000 2500.000 2.5000000 1000.0000 C040 NA 5 1 ng/g 0.6600000 NA 0.0200000 sd technical 5 1 0.5700000 NA 0.0200000 technical NA ng/g 0.04 0.11 Dependent Table 4 No NA NA NA NA 2.5000000
F008 Luo_2019 2019 Korea E322 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFUnDA 11 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500.000 2500.000 2.5000000 1000.0000 C040 NA 5 1 ng/g 0.9300000 NA 0.0100000 sd technical 5 1 0.7900000 NA 0.0200000 technical NA ng/g 0.08 0.25 Dependent Table 4 No NA NA NA NA 2.5000000
F008 Luo_2019 2019 Korea E323 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFDoDA 12 NA No Boiling water-based Boiled with radish 100 600 Yes No NA 2500.000 2500.000 2.5000000 1000.0000 C040 NA 5 1 ng/g 0.2500000 NA 0.0200000 sd technical 5 1 0.2300000 NA 0.0100000 technical NA ng/g 0.06 0.19 Dependent Table 4 No NA NA NA NA 2.5000000
F008 Luo_2019 2019 Korea E324 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFTrA 13 NA No Boiling water-based Boiled with radish 100 600 Yes No NA 2500.000 2500.000 2.5000000 1000.0000 C040 NA 5 1 ng/g 1.1200000 NA 0.0600000 sd technical 5 1 1.3800000 NA 0.0900000 technical NA ng/g 0.05 0.16 Dependent Table 4 No NA NA NA NA 2.5000000
F008 Luo_2019 2019 Korea E325 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFTA 14 NA No Boiling water-based Boiled with radish 100 600 Yes No NA 2500.000 2500.000 2.5000000 1000.0000 C040 NA 5 1 ng/g 0.2800000 NA 0.0100000 sd technical 5 1 0.2600000 NA 0.0200000 technical NA ng/g 0.05 0.15 Dependent Table 4 No NA NA NA NA 2.5000000
F008 Luo_2019 2019 Korea E326 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFHxS 6 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500.000 2500.000 2.5000000 1000.0000 C040 NA 5 1 ng/g 0.4800000 NA 0.0300000 sd technical 5 1 0.3300000 NA 0.0300000 technical NA ng/g 0.08 0.25 Dependent Table 4 No NA NA NA NA 2.5000000
F008 Luo_2019 2019 Korea E327 Swimming crab Portunus trituberculatus invertebrate crustacea NA PFDS 10 NA No Boiling water-based Boiled with radish 100 600 Yes No NA 2500.000 2500.000 2.5000000 1000.0000 C040 NA 5 1 ng/g 0.0400000 NA 0.0100000 sd technical 5 1 0.0400000 NA 0.0100000 technical NA ng/g 0.09 0.27 Dependent Table 4 No NA NA NA NA 2.5000000
F008 Luo_2019 2019 Korea E328 Swimming crab Portunus trituberculatus invertebrate crustacea NA FOSA 8 NA Yes Boiling water-based Boiled with radish 100 600 Yes No NA 2500.000 2500.000 2.5000000 1000.0000 C040 NA 5 1 ng/g 1.5400000 NA 0.0900000 sd technical 5 1 2.5500000 NA 0.1900000 technical NA ng/g 0.04 0.11 Dependent Table 4 No NA NA NA NA 2.5000000
F010 Sungur_2019 2019 Turkey E329 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C041 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.1590000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied ML - note shared controls for differend cooking times and methods NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E330 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C042 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.1170000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E331 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C043 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.0790000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E332 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C044 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.1420000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E333 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C045 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.1160000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E334 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C046 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.0980000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E335 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C047 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.1400000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E336 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C048 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.1330000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E337 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C049 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.0710000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E338 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C050 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.2010000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E339 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C051 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.0590000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E340 Bluefish Pomatomus saltator vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C052 NA 10 1 ng/g 0.2320000 NA 0.0010000 sd technical 10 1 0.0480000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E341 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C041 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 14.7000000 NA 0.0090000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E342 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C042 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 9.3500000 NA 0.0080000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E343 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C043 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 3.6600000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E344 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C044 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 5.6300000 NA 0.0050000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E345 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C045 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 4.5000000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E346 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C046 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 3.7700000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E347 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C047 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 8.2800000 NA 0.0070000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E348 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C048 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 6.6200000 NA 0.0060000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E349 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C049 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 3.4800000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E350 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C050 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 4.4900000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E351 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C051 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 3.0500000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E352 Bluefish Pomatomus saltator vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C052 NA 10 1 ng/g 24.0000000 NA 0.0110000 sd technical 10 1 2.8300000 NA 0.0030000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E353 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C053 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.1960000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E354 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C054 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.1180000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E355 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C055 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.0840000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E356 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C056 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.2030000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E357 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C057 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.1390000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E358 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C058 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.1040000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E359 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C059 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.2070000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E360 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C060 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.0970000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E361 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C061 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.0820000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E362 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C062 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.1960000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E363 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C063 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.0510000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E364 Red mullet Mullus barbatus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C064 NA 10 1 ng/g 0.2140000 NA 0.0010000 sd technical 10 1 0.2550000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E365 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C053 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 4.7800000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E366 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C054 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 3.5000000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E367 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C055 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 1.5100000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E368 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C056 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 7.0500000 NA 0.0060000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E369 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C057 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 2.4700000 NA 0.0030000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E370 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C058 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 1.7600000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E371 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C059 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 3.0300000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E372 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C060 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 2.0400000 NA 0.0030000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E373 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C061 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 1.2300000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E374 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C062 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 4.2800000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E375 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C063 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 2.7800000 NA 0.0030000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E376 Red mullet Mullus barbatus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C064 NA 10 1 ng/g 8.9200000 NA 0.0070000 sd technical 10 1 1.0200000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E377 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C065 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.2420000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E378 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C066 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.1870000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E379 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C067 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.0960000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E380 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C068 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.1750000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E381 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C069 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.1530000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E382 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C070 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.0980000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E383 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C071 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.1890000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E384 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C072 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.1320000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E385 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C073 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.0930000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E386 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C074 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.1810000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E387 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C075 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.0880000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E388 Whitefish Salmo trutta vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C076 NA 10 1 ng/g 0.2550000 NA 0.0010000 sd technical 10 1 0.0660000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E389 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C065 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 4.1500000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E390 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C066 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 2.6500000 NA 0.0030000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E391 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C067 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 1.2300000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E392 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C068 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 4.4400000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E393 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C069 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 2.3600000 NA 0.0030000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E394 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C070 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 1.6500000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E395 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C071 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 3.6800000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E396 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C072 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 1.7300000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E397 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C073 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 0.9200000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E398 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C074 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 4.0300000 NA 0.0040000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E399 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C075 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 1.9700000 NA 0.0020000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E400 Whitefish Salmo trutta vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C076 NA 10 1 ng/g 5.0700000 NA 0.0040000 sd technical 10 1 0.8400000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E401 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C077 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.2020000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E402 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C078 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.1280000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E403 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C079 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.0920000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E404 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C080 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.1580000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E405 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C081 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.1210000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E406 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C082 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.0980000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E407 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C083 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.1680000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E408 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C084 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.1340000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E409 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C085 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.0910000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E410 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C086 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.1740000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E411 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C087 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.0960000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E412 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C088 NA 10 1 ng/g 0.2380000 NA 0.0010000 sd technical 10 1 0.0440000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E413 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C077 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.2760000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E414 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C078 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.1750000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E415 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C079 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.0900000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E416 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C080 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.3110000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E417 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C081 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.2840000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E418 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C082 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.0940000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E419 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C083 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.2970000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E420 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C084 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.1610000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E421 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C085 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.0850000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E422 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C086 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.1640000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E423 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C087 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.0930000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E424 Common pandora Pagellus erythrinus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C088 NA 10 1 ng/g 0.4070000 NA 0.0010000 sd technical 10 1 0.0670000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E425 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C089 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1970000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E426 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C090 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1460000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E427 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C091 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.0900000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E428 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C092 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.2120000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E429 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C093 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1220000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E430 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C094 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.0940000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E431 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C095 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1470000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E432 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C096 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1280000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E433 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C097 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.0690000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E434 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C098 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1450000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E435 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C099 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.1020000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E436 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C100 NA 10 1 ng/g 0.2980000 NA 0.0010000 sd technical 10 1 0.0420000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E437 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C089 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.3720000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E438 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C090 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.2510000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E439 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C091 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.0940000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E440 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C092 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.2540000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E441 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C093 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.1800000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E442 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C094 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.0970000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E443 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C095 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.3260000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E444 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C096 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.1550000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E445 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C097 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.0630000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E446 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C098 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.3580000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E447 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C099 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.1970000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E448 Flathead grey mullet Mugil cephalus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C100 NA 10 1 ng/g 0.4180000 NA 0.0010000 sd technical 10 1 0.0560000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E449 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C101 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.1470000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E450 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C102 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.1150000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E451 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C103 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.0500000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E452 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C104 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.1480000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E453 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C105 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.1070000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E454 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Baking oil-based NA 160 1200 No No NA 300.000 300.000 30.0000000 10.0000 C106 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.0570000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E455 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C107 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.1210000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E456 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C108 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.0950000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E457 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C109 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.0430000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E458 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C110 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.1150000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E459 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C111 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.0820000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E460 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C112 NA 10 1 ng/g 0.1530000 NA 0.0010000 sd technical 10 1 0.0330000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E461 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C101 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.6640000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E462 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C102 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.3120000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E463 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C103 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.0990000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E464 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C104 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.6180000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E465 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C105 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.3780000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E466 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C106 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.1070000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E467 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C107 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.5980000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E468 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C108 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.4020000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E469 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C109 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.0970000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E470 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C110 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.6180000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E471 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C111 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.2460000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E472 Atlantic mackerel Scomber scombrus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C112 NA 10 1 ng/g 0.7860000 NA 0.0010000 sd technical 10 1 0.0890000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E473 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C113 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0980000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E474 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C114 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0620000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E475 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C115 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0430000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E476 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C116 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0800000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E477 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C117 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0600000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E478 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C118 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0450000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E479 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C119 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0980000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E480 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C120 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0700000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E481 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C121 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0340000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E482 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C122 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0650000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E483 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C123 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0580000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E484 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C124 NA 10 1 ng/g 0.1080000 NA 0.0010000 sd technical 10 1 0.0320000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E485 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C113 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.1540000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E486 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C114 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.1080000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E487 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C115 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.0920000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E488 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C116 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.1470000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E489 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C117 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.1020000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E490 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C118 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.0940000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E491 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C119 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.1260000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E492 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C120 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.0990000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E493 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C121 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.0520000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E494 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C122 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.1020000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E495 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C123 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.0760000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E496 Pike-perch Dicentrarchus labrax vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C124 NA 10 1 ng/g 0.2740000 NA 0.0010000 sd technical 10 1 0.0490000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E497 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C125 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.1450000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E498 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C126 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.1130000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E499 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C127 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.0540000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E500 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C128 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.1520000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E501 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C129 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.1280000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E502 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C130 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.0610000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E503 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C131 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.1220000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E504 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C132 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.0920000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E505 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C133 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.0490000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E506 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C134 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.1180000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E507 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C135 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.0890000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E508 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C136 NA 10 1 ng/g 0.1810000 NA 0.0010000 sd technical 10 1 0.0440000 NA 0.0010000 technical 3 ng/g 0.009 0.030 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E509 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 600 Yes No NA 300.000 300.000 30.0000000 10.0000 C125 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.3570000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E510 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 900 Yes No NA 300.000 300.000 30.0000000 10.0000 C126 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.2100000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E511 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Boiling water-based NA 100 1200 Yes No NA 300.000 300.000 30.0000000 10.0000 C127 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.0920000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E512 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 600 No No NA NA 0.000 NA NA C128 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.2560000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E513 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 900 No No NA NA 0.000 NA NA C129 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.1840000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E514 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Baking No liquid NA 160 1200 No No NA NA 0.000 NA NA C130 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.0990000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 0.0000000
F010 Sungur_2019 2019 Turkey E515 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 600 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C131 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.3440000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E516 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 900 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C132 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.1480000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E517 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in olive oil 160 1200 No Yes olive oil 300.000 300.000 30.0000000 10.0000 C133 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.0820000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E518 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 600 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C134 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.3410000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E519 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 900 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C135 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.1920000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F010 Sungur_2019 2019 Turkey E520 Mediterranean sand smelt Atherina hepsetus vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based in sunflower oil 160 1200 No Yes sunflower oil 300.000 300.000 30.0000000 10.0000 C136 NA 10 1 ng/g 0.4760000 NA 0.0010000 sd technical 10 1 0.0540000 NA 0.0010000 technical 3 ng/g 0.006 0.020 Shared control Table 3 No Authors replied NA NA NA 30.0000000
F011 Taylor_2019 2019 Australia E521 Dusky flathead Platycephalus fuscus vertebrate marine fish 21.470000 PFHxS 6 linear Yes Baking oil-based NA 75 600 No Yes olive oil 20.000 20.000 0.3976934 50.2900 C137 Contaminated site 4 4 ng/g 0.9673000 NA 1.0026000 sd biological 4 4 1.4745000 NA 1.7430000 biological 1 ng/g 0.023508736 0.078362453 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied ML - check empty fields, why SE/SD field is NA? 1.0026000 1.7430000 0.3976934
F011 Taylor_2019 2019 Australia E522 Dusky flathead Platycephalus fuscus vertebrate marine fish 21.470000 PFOS 8 linear Yes Baking oil-based NA 75 600 No Yes olive oil 20.000 20.000 0.3976934 50.2900 C137 Contaminated site 6 6 ng/g 75.6360000 NA 133.7000000 sd biological 6 6 84.5499000 NA 130.5000000 biological 1 ng/g 0.023185477 0.077284922 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 133.7000000 130.5000000 0.3976934
F011 Taylor_2019 2019 Australia E523 Dusky flathead Platycephalus fuscus vertebrate marine fish 21.470000 PFOS 8 linear Yes Baking oil-based NA 75 600 No Yes olive oil 20.000 20.000 0.4610420 43.3800 C138 Clean site 3 3 ng/g 0.0894000 NA 0.0339000 sd biological 3 3 0.1210000 NA 0.0390000 biological 1 ng/g 0.023185477 0.077284922 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0339000 0.0390000 0.4610420
F011 Taylor_2019 2019 Australia E526 Dusky flathead Platycephalus fuscus vertebrate marine fish 21.470000 PFDS 10 linear Yes Baking oil-based NA 75 600 No Yes olive oil 20.000 20.000 0.3976934 50.2900 C137 Contaminated site 2 2 ng/g 0.1391000 NA 0.0247000 sd biological 2 2 0.3760000 NA 0.0240000 biological 1 ng/g 0.030122517 0.10040839 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0247000 0.0240000 0.3976934
F011 Taylor_2019 2019 Australia E527 Dusky flathead Platycephalus fuscus vertebrate marine fish 21.470000 FOSA 8 NA Yes Baking oil-based NA 75 600 No Yes olive oil 20.000 20.000 0.3976934 50.2900 C137 Contaminated site 2 2 ng/g 0.0749000 <LOQ NA Not available bacause Mc/Me is below LOD/LOQ NA 2 2 0.1985000 NA 0.0120000 biological 1 ng/g 0.034582913 0.115276378 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA 0.0120000 0.3976934
F011 Taylor_2019 2019 Australia E528 Dusky flathead Platycephalus fuscus vertebrate marine fish 18.640000 PFHxS 6 linear Yes Frying oil-based NA 82 120 No Yes olive oil 40.000 40.000 0.7696748 51.9700 C140 Contaminated site 5 5 ng/g 0.7841000 NA 0.9602000 sd biological 5 5 0.8414000 NA 1.0420000 biological 1 ng/g 0.023508736 0.078362453 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.9602000 1.0420000 0.7696748
F011 Taylor_2019 2019 Australia E529 Dusky flathead Platycephalus fuscus vertebrate marine fish 18.640000 PFOS 8 linear Yes Frying oil-based NA 82 120 No Yes olive oil 40.000 40.000 0.7696748 51.9700 C139 Contaminated site 6 6 ng/g 75.6360000 NA 133.7000000 sd biological 6 6 70.8427000 NA 106.0000000 biological 1 ng/g 0.023185477 0.077284922 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 133.7000000 106.0000000 0.7696748
F011 Taylor_2019 2019 Australia E530 Dusky flathead Platycephalus fuscus vertebrate marine fish 18.640000 PFOS 8 linear Yes Frying oil-based NA 82 120 No Yes olive oil 40.000 40.000 0.9220839 43.3800 C140 Clean site 2 2 ng/g 0.1090000 NA 0.0014000 sd biological 2 2 0.2005000 NA 0.0730000 biological 1 ng/g 0.023185477 0.077284922 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0014000 0.0730000 0.9220839
F011 Taylor_2019 2019 Australia E533 Dusky flathead Platycephalus fuscus vertebrate marine fish 18.640000 FOSA 8 NA Yes Frying oil-based NA 82 120 No Yes olive oil 40.000 40.000 0.7696748 51.9700 C139 Contaminated site 4 4 ng/g 0.1070000 NA 0.0397000 sd biological 4 4 0.2540000 NA 0.1320000 biological 1 ng/g 0.034582913 0.115276378 Shared control Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0397000 0.1320000 0.7696748
F011 Taylor_2019 2019 Australia E534 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFHxA 6 linear No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500.000 500.000 45.3309157 11.0300 C141 Contaminated site 3 3 ng/g 0.1513000 NA 0.0306000 sd biological 3 3 0.0729200 NA 0.0210000 biological 1 ng/g 0.028099467 0.093664888 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0306000 0.0210000 45.3309157
F011 Taylor_2019 2019 Australia E535 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFHpA 7 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500.000 500.000 45.3309157 11.0300 C141 Contaminated site 6 6 ng/g 0.2070000 NA 0.1445000 sd biological 6 6 0.1086500 NA 0.0520000 biological 1 ng/g 0.01867491 0.0622497 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.1445000 0.0520000 45.3309157
F011 Taylor_2019 2019 Australia E536 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFOA 8 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500.000 500.000 45.3309157 11.0300 C141 Contaminated site 6 6 ng/g 0.4279000 NA 0.2601000 sd biological 6 6 0.2316000 NA 0.1070000 biological 1 ng/g 0.014519809 0.048399364 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.2601000 0.1070000 45.3309157
F011 Taylor_2019 2019 Australia E537 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFOA 8 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500.000 500.000 40.4858300 12.3500 C142 Clean site 4 4 ng/g 0.0433000 NA 0.0137000 sd biological 4 4 0.0712200 NA 0.0660000 biological 1 ng/g 0.014519809 0.048399364 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0137000 0.0660000 40.4858300
F011 Taylor_2019 2019 Australia E538 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFUnDA 11 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500.000 500.000 45.3309157 11.0300 C141 Contaminated site 4 4 ng/g 0.1128000 NA 0.0093000 sd biological 4 4 0.0579700 <LOQ NA NA 1 ng/g 0.026755217 0.089184057 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0093000 NA 45.3309157
F011 Taylor_2019 2019 Australia E539 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFUnDA 11 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500.000 500.000 40.4858300 12.3500 C142 Clean site 1 1 ng/g 0.1047000 NA NA sd biological 1 1 0.0579700 <LOQ NA NA 1 ng/g 0.026755217 0.089184057 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA NA 40.4858300
F011 Taylor_2019 2019 Australia E540 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFDoDA 12 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500.000 500.000 45.3309157 11.0300 C141 Contaminated site 1 1 ng/g 0.0802000 <LOQ NA Not available bacause Mc/Me is below LOD/LOQ NA 1 1 0.1279700 No sd, as N = 1 NA NA 1 ng/g 0.037026547 0.123421824 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA NA 45.3309157
F011 Taylor_2019 2019 Australia E541 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFDoDA 12 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500.000 500.000 40.4858300 12.3500 C142 Clean site 1 1 ng/g 0.1230000 NA NA sd biological 1 1 0.0802200 <LOQ NA NA 1 ng/g 0.037026547 0.123421824 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA NA 40.4858300
F011 Taylor_2019 2019 Australia E542 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFHxS 6 linear No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500.000 500.000 45.3309157 11.0300 C141 Contaminated site 6 6 ng/g 0.5991000 NA 0.2053000 sd biological 6 6 0.3865700 NA 0.0790000 biological 1 ng/g 0.023508736 0.078362453 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.2053000 0.0790000 45.3309157
F011 Taylor_2019 2019 Australia E543 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFHxS 6 linear No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500.000 500.000 40.4858300 12.3500 C142 Clean site 1 1 ng/g 0.1230000 <LOQ NA Not available bacause Mc/Me is below LOD/LOQ NA 1 1 0.0809900 No sd, as N = 1 NA NA 1 ng/g 0.023508736 0.078362453 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA NA 40.4858300
F011 Taylor_2019 2019 Australia E544 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFOS 8 linear No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500.000 500.000 45.3309157 11.0300 C141 Contaminated site 6 6 ng/g 5.0500000 NA 0.4637000 sd biological 6 6 5.5333300 NA 0.8290000 biological 1 ng/g 0.023185477 0.077284922 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.4637000 0.8290000 45.3309157
F011 Taylor_2019 2019 Australia E545 Blue swimmer crab Portunus armatus invertebrate crustacea NA PFOS 8 linear No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500.000 500.000 40.4858300 12.3500 C142 Clean site 6 6 ng/g 0.1917000 NA 0.2129000 sd biological 6 6 0.1917100 NA 0.2360000 biological 1 ng/g 0.023185477 0.077284922 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.2129000 0.2360000 40.4858300
F011 Taylor_2019 2019 Australia E548 Blue swimmer crab Portunus armatus invertebrate crustacea NA FOSA 8 NA No Boiling water-based in saltwater (8.5 g/L) 100 420 Yes No NA 500.000 500.000 45.3309157 11.0300 C141 Contaminated site 6 6 ng/g 0.3112000 NA 0.1413000 sd biological 6 6 0.3215300 NA 0.0990000 biological 1 ng/g 0.034582913 0.115276378 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.1413000 0.0990000 45.3309157
F011 Taylor_2019 2019 Australia E549 School prawn Metapenaeus macleayi invertebrate crustacea NA PFHpA 7 NA Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500.000 500.000 7.7375426 64.6200 C143 Contaminated site 10 1 ng/g 0.0802000 <LOQ NA Not available bacause Mc/Me is below LOD/LOQ NA 10 1 0.1279700 NA NA biological 1 ng/g 0.01867491 0.0622497 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA NA 7.7375426
F011 Taylor_2019 2019 Australia E550 School prawn Metapenaeus macleayi invertebrate crustacea NA PFOA 8 NA Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500.000 500.000 7.7375426 64.6200 C143 Contaminated site 10 1 ng/g 0.2229000 NA 0.0668000 sd biological 60 6 0.4689700 NA 0.1040000 biological 1 ng/g 0.014519809 0.048399364 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0668000 0.1040000 7.7375426
F011 Taylor_2019 2019 Australia E551 School prawn Metapenaeus macleayi invertebrate crustacea NA PFNA 9 NA Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500.000 500.000 7.7375426 64.6200 C143 Contaminated site 10 1 ng/g 0.0910000 <LOQ NA Not available bacause Mc/Me is below LOD/LOQ NA 60 6 0.2330900 NA 0.0370000 biological 1 ng/g 0.036013573 0.120045244 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA 0.0370000 7.7375426
F011 Taylor_2019 2019 Australia E552 School prawn Metapenaeus macleayi invertebrate crustacea NA PFDA 10 NA Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500.000 500.000 7.7375426 64.6200 C143 Contaminated site 10 1 ng/g 0.0854000 <LOQ NA Not available bacause Mc/Me is below LOD/LOQ NA 50 5 0.1877100 NA 0.0530000 biological 1 ng/g 0.039417906 0.131393021 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA NA 0.0530000 7.7375426
F011 Taylor_2019 2019 Australia E553 School prawn Metapenaeus macleayi invertebrate crustacea NA PFHxS 6 linear Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500.000 500.000 7.7375426 64.6200 C143 Contaminated site 10 1 ng/g 2.3305000 NA 1.3905000 sd biological 60 6 6.3161900 NA 1.6280000 biological 1 ng/g 0.023508736 0.078362453 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 1.3905000 1.6280000 7.7375426
F011 Taylor_2019 2019 Australia E554 School prawn Metapenaeus macleayi invertebrate crustacea NA PFOS 8 linear Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500.000 500.000 7.7375426 64.6200 C143 Contaminated site 10 1 ng/g 7.4167000 NA 2.8414000 sd biological 60 6 16.1667000 NA 3.8690000 biological 1 ng/g 0.023185477 0.077284922 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 2.8414000 3.8690000 7.7375426
F011 Taylor_2019 2019 Australia E555 School prawn Metapenaeus macleayi invertebrate crustacea NA PFOS 8 linear Yes Boiling water-based in saltwater (8.5 g/L) 100 240 Yes No NA 500.000 500.000 12.5376128 39.8800 C144 Clean site 10 1 ng/g 0.0562000 NA 0.0133000 sd biological 50 5 0.1180000 NA 0.0290000 biological 1 ng/g 0.023185477 0.077284922 Dependent Taylor.et.al_2019_AdditionalInofrmation.from.Jenny_2.PFAS_Biota Yes Authors replied NA 0.0133000 0.0290000 12.5376128
F013 Vassiliadou_2015 2015 Greece E557 Anchovy Engraulis encrasicolus vertebrate marine fish 72.739187 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.9000000 333.3333 C145 NA 23 1 ng/g 1.5000000 NA 0.0400000 sd technical 30 1 1.7500000 NA 0.0500000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author, mass might not be correct as it back-calculated from averages (not included in paper) ML - why “Se_technical_biological” is coded as “sd”? “If_technical_how_many” needs a number. Shared control between differend cooking methods NA NA 0.9000000
F013 Vassiliadou_2015 2015 Greece E558 Anchovy Engraulis encrasicolus vertebrate marine fish 72.739187 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.9000000 333.3333 C145 NA 23 1 ng/g 1.8600000 NA 0.1900000 sd technical 30 1 2.9900000 NA 0.2200000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.9000000
F013 Vassiliadou_2015 2015 Greece E559 Anchovy Engraulis encrasicolus vertebrate marine fish 72.739187 PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.9000000 333.3333 C145 NA 23 1 ng/g 3.0600000 NA 0.1000000 sd technical 30 1 6.6200000 NA 0.1400000 technical 1 ng/g 0.49 1.48 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.9000000
F013 Vassiliadou_2015 2015 Greece E560 Bogue Boops boops vertebrate marine fish 18.354430 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.3600000 833.3333 C146 NA 12 1 ng/g 0.2400000 NA 0.0300000 sd technical 30 1 0.4400000 NA 0.0200000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.3600000
F013 Vassiliadou_2015 2015 Greece E561 Bogue Boops boops vertebrate marine fish 18.354430 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.3600000 833.3333 C146 NA 12 1 ng/g 0.5600000 NA 0.0800000 sd technical 30 1 1.1200000 NA 0.0300000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.3600000
F013 Vassiliadou_2015 2015 Greece E562 Bogue Boops boops vertebrate marine fish 18.354430 PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.3600000 833.3333 C146 NA 12 1 ng/g 0.8200000 NA 0.0400000 sd technical 30 1 1.2700000 NA 0.0600000 technical 1 ng/g 0.49 1.48 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.3600000
F013 Vassiliadou_2015 2015 Greece E563 Hake Merluccius merluccius vertebrate marine fish 36.000000 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4300000 697.6744 C147 NA 20 1 ng/g 0.4200000 NA 0.0500000 sd technical 10 1 0.7000000 LOD NA technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4300000
F013 Vassiliadou_2015 2015 Greece E564 Hake Merluccius merluccius vertebrate marine fish 36.000000 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4300000 697.6744 C147 NA 20 1 ng/g 0.6200000 NA 0.0800000 sd technical 10 1 0.1000000 <LOD NA NA 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4300000
F013 Vassiliadou_2015 2015 Greece E565 Hake Merluccius merluccius vertebrate marine fish 36.000000 PFBS 4 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4300000 697.6744 C147 NA 20 1 ng/g 0.4500000 NA 0.0700000 sd technical 10 1 0.8300000 NA 0.0300000 technical 1 ng/g 0.57 1.7 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4300000
F013 Vassiliadou_2015 2015 Greece E566 Hake Merluccius merluccius vertebrate marine fish 36.000000 PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4300000 697.6744 C147 NA 20 1 ng/g 0.8400000 NA 0.1000000 sd technical 10 1 1.2400000 NA 0.0600000 technical 1 ng/g 0.49 1.48 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4300000
F013 Vassiliadou_2015 2015 Greece E567 Picarel Spicara smaris vertebrate marine fish 44.037940 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4700000 638.2979 C148 NA 20 1 ng/g 0.7000000 NA 0.0900000 sd technical 30 1 1.3500000 NA 0.0800000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4700000
F013 Vassiliadou_2015 2015 Greece E568 Picarel Spicara smaris vertebrate marine fish 44.037940 PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4700000 638.2979 C148 NA 20 1 ng/g 20.3700000 NA 2.4700000 sd technical 30 1 44.6900000 NA 3.9300000 technical 1 ng/g 0.49 1.48 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4700000
F013 Vassiliadou_2015 2015 Greece E569 Sand smelt Atherina boyeri vertebrate marine fish 79.108280 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.5200000 576.9231 C149 NA 39 1 ng/g 0.3500000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ NA 30 1 0.7400000 NA 0.0900000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.5200000
F013 Vassiliadou_2015 2015 Greece E570 Sand smelt Atherina boyeri vertebrate marine fish 79.108280 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.5200000 576.9231 C149 NA 39 1 ng/g 1.0800000 NA 0.0300000 sd technical 30 1 1.9800000 NA 0.0400000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.5200000
F013 Vassiliadou_2015 2015 Greece E571 Sand smelt Atherina boyeri vertebrate marine fish 79.108280 PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.5200000 576.9231 C149 NA 39 1 ng/g 1.1600000 NA 0.0500000 sd technical 30 1 3.0100000 NA 0.1300000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.5200000
F013 Vassiliadou_2015 2015 Greece E572 Sardine Sardina pilchardus vertebrate marine fish 57.258065 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.8800000 340.9091 C150 NA 14 1 ng/g 0.1000000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ NA 30 1 0.9300000 NA 0.0300000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.8800000
F013 Vassiliadou_2015 2015 Greece E573 Striped mullet Mullus barbatus vertebrate marine fish 61.316212 PFNA 9 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.5700000 526.3158 C151 NA 15 1 ng/g 0.6000000 NA 0.0300000 sd technical 30 1 0.5700000 NA 0.1100000 technical 1 ng/g 0.42 1.25 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.5700000
F013 Vassiliadou_2015 2015 Greece E574 Striped mullet Mullus barbatus vertebrate marine fish 61.316212 PFDA 10 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.5700000 526.3158 C151 NA 15 1 ng/g 0.6500000 NA 0.0600000 sd technical 30 1 0.5600000 NA 0.0700000 technical 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.5700000
F013 Vassiliadou_2015 2015 Greece E575 Striped mullet Mullus barbatus vertebrate marine fish 61.316212 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.5700000 526.3158 C151 NA 15 1 ng/g 1.0500000 NA 0.1300000 sd technical 30 1 0.7300000 NA 0.2000000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.5700000
F013 Vassiliadou_2015 2015 Greece E576 Striped mullet Mullus barbatus vertebrate marine fish 61.316212 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.5700000 526.3158 C151 NA 15 1 ng/g 0.1000000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ technical 30 1 1.3800000 NA 0.0700000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.5700000
F013 Vassiliadou_2015 2015 Greece E577 Striped mullet Mullus barbatus vertebrate marine fish 61.316212 PFOS 8 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.5700000 526.3158 C151 NA 15 1 ng/g 5.6600000 NA 0.1500000 sd technical 30 1 0.1000000 <LOD NA NA 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.5700000
F013 Vassiliadou_2015 2015 Greece E578 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFPeA 5 NA No Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4800000 625.0000 C152 NA 30 1 ng/g 4.9400000 NA 0.2600000 sd technical 40 1 14.8800000 NA 1.6100000 technical 1 ng/g 0.39 1.17 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4800000
F013 Vassiliadou_2015 2015 Greece E579 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFOA 8 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4800000 625.0000 C152 NA 30 1 ng/g 0.3000000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ technical 40 1 0.9900000 NA 0.2100000 technical 1 ng/g 0.6 1.82 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4800000
F013 Vassiliadou_2015 2015 Greece E580 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFNA 9 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4800000 625.0000 C152 NA 30 1 ng/g 1.2700000 NA 0.0700000 sd technical 40 1 1.5200000 NA 0.1100000 technical 1 ng/g 0.42 1.25 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4800000
F013 Vassiliadou_2015 2015 Greece E581 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFDA 10 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4800000 625.0000 C152 NA 30 1 ng/g 1.7300000 NA 0.0800000 sd technical 40 1 1.8100000 NA 0.1900000 technical 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4800000
F013 Vassiliadou_2015 2015 Greece E582 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4800000 625.0000 C152 NA 30 1 ng/g 2.7600000 NA 0.2100000 sd technical 40 1 6.8200000 NA 0.2200000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4800000
F013 Vassiliadou_2015 2015 Greece E583 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4800000 625.0000 C152 NA 30 1 ng/g 1.3600000 NA 0.0900000 sd technical 40 1 2.3100000 NA 0.0900000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4800000
F013 Vassiliadou_2015 2015 Greece E584 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFBS 4 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4800000 625.0000 C152 NA 30 1 ng/g 1.3700000 NA 0.1600000 sd technical 40 1 0.2850000 <LOD NA NA 1 ng/g 0.57 1.7 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4800000
F013 Vassiliadou_2015 2015 Greece E585 Shrimp Parapenaeus longirostris vertebrate marine fish NA PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.4800000 625.0000 C152 NA 30 1 ng/g 5.1500000 NA 0.3900000 sd technical 40 1 8.0200000 NA 0.4200000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.4800000
F013 Vassiliadou_2015 2015 Greece E586 Squid Loligo vulgaris vertebrate marine fish 47.867299 PFPeA 5 NA No Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.3900000 769.2308 C153 NA 18 1 ng/g 0.1950000 <LOD NA sd technical 40 1 5.0600000 NA 0.1900000 technical 1 ng/g 0.39 1.17 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.3900000
F013 Vassiliadou_2015 2015 Greece E587 Squid Loligo vulgaris vertebrate marine fish 47.867299 PFDA 10 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.3900000 769.2308 C153 NA 18 1 ng/g 0.3450000 <LOD NA sd technical 40 1 0.5100000 NA 0.0400000 technical 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.3900000
F013 Vassiliadou_2015 2015 Greece E588 Squid Loligo vulgaris vertebrate marine fish 47.867299 PFUnDA 11 NA Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.3900000 769.2308 C153 NA 18 1 ng/g 0.3500000 <LOD NA sd technical 40 1 1.0400000 NA 0.0200000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.3900000
F013 Vassiliadou_2015 2015 Greece E589 Squid Loligo vulgaris vertebrate marine fish 47.867299 PFDoDA 12 NA No Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.3900000 769.2308 C153 NA 18 1 ng/g 0.1000000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ NA 40 1 1.6500000 NA 0.0700000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.3900000
F013 Vassiliadou_2015 2015 Greece E590 Squid Loligo vulgaris vertebrate marine fish 47.867299 PFOS 8 linear Yes Frying oil-based NA 170 NA No Yes olive oil 300.000 300.000 0.3900000 769.2308 C153 NA 18 1 ng/g 0.1000000 <LOD NA sd technical 40 1 1.5600000 NA 0.1700000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.3900000
F013 Vassiliadou_2015 2015 Greece E591 Anchovy Engraulis encrasicolus vertebrate marine fish 33.158585 PFDA 10 NA Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C154 NA 30 1 ng/g 0.3450000 <LOD NA sd technical 30 1 0.8300000 NA 0.0100000 technical 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E592 Anchovy Engraulis encrasicolus vertebrate marine fish 33.158585 PFUnDA 11 NA Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C154 NA 30 1 ng/g 1.5000000 NA 0.0400000 sd technical 30 1 2.7300000 NA 0.1300000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E593 Anchovy Engraulis encrasicolus vertebrate marine fish 33.158585 PFDoDA 12 NA No Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C154 NA 30 1 ng/g 1.8600000 NA 0.1900000 sd technical 30 1 3.5200000 NA 0.1000000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E594 Anchovy Engraulis encrasicolus vertebrate marine fish 33.158585 PFOS 8 linear Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C154 NA 30 1 ng/g 3.0600000 NA 0.1000000 sd technical 30 1 6.2900000 NA 0.3400000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E595 Bogue Boops boops vertebrate marine fish 7.436709 PFUnDA 11 NA Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C155 NA 30 1 ng/g 0.2400000 NA 0.0300000 sd technical 30 1 0.4300000 NA 0.0300000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E596 Bogue Boops boops vertebrate marine fish 7.436709 PFDoDA 12 NA No Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C155 NA 30 1 ng/g 0.5600000 NA 0.0800000 sd technical 30 1 0.6300000 NA 0.0200000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E597 Bogue Boops boops vertebrate marine fish 7.436709 PFOS 8 linear Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C155 NA 30 1 ng/g 0.8200000 NA 0.0400000 sd technical 30 1 0.8700000 NA 0.0700000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E598 Hake Merluccius merluccius vertebrate marine fish 18.909091 PFDA 10 NA Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C156 NA 10 1 ng/g 0.3450000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ NA 10 1 0.8200000 NA 0.0300000 technical 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E599 Hake Merluccius merluccius vertebrate marine fish 18.909091 PFUnDA 11 NA Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C156 NA 10 1 ng/g 0.4200000 NA 0.0500000 sd technical 10 1 1.1100000 NA 0.1500000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E600 Hake Merluccius merluccius vertebrate marine fish 18.909091 PFDoDA 12 NA No Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C156 NA 10 1 ng/g 0.6200000 NA 0.0800000 sd technical 10 1 1.8900000 NA 0.0500000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E601 Hake Merluccius merluccius vertebrate marine fish 18.909091 PFBS 4 NA Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C156 NA 10 1 ng/g 0.4500000 NA 0.0700000 sd technical 10 1 0.2850000 <LOD NA NA 1 ng/g 0.57 1.7 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E602 Hake Merluccius merluccius vertebrate marine fish 18.909091 PFOS 8 linear Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C156 NA 10 1 ng/g 0.8400000 NA 0.1000000 sd technical 10 1 2.4000000 NA 0.1300000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E603 Sardine Sardina pilchardus vertebrate marine fish 9.946237 PFDA 10 NA Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C157 NA 30 1 ng/g 0.3450000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ NA 30 1 0.8700000 NA 0.0300000 technical 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E604 Sardine Sardina pilchardus vertebrate marine fish 9.946237 PFUnDA 11 NA Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C157 NA 30 1 ng/g 0.3500000 <LOD NA sd technical 30 1 1.7000000 NA 0.1300000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E605 Sardine Sardina pilchardus vertebrate marine fish 9.946237 PFDoDA 12 NA No Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C157 NA 30 1 ng/g 0.1000000 <LOD NA sd technical 30 1 3.1900000 NA 0.0900000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E606 Striped mullet Mullus barbatus vertebrate marine fish 17.656501 PFNA 9 NA Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C158 NA 30 1 ng/g 0.6000000 NA 0.0300000 sd technical 30 1 0.5000000 NA 0.0500000 technical 1 ng/g 0.42 1.25 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E607 Striped mullet Mullus barbatus vertebrate marine fish 17.656501 PFDA 10 NA Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C158 NA 30 1 ng/g 0.6500000 NA 0.0600000 sd technical 30 1 0.3450000 <LOD NA NA 1 ng/g 0.69 2.08 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E608 Striped mullet Mullus barbatus vertebrate marine fish 17.656501 PFUnDA 11 NA Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C158 NA 30 1 ng/g 1.0500000 NA 0.1300000 sd technical 30 1 0.8200000 NA 0.0200000 technical 1 ng/g 0.7 2.11 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E609 Striped mullet Mullus barbatus vertebrate marine fish 17.656501 PFOS 8 linear Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C158 NA 30 1 ng/g 5.6600000 NA 0.1500000 sd technical 30 1 10.2300000 NA 0.5300000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E610 Squid Loligo vulgaris vertebrate mollusca 24.289099 PFOA 8 NA No Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C159 NA 40 1 ng/g 0.3000000 <LOD NA Not available bacause Mc/Me is below LOD/LOQ NA 40 1 0.4000000 NA 0.0100000 technical 1 ng/g 0.6 1.82 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E611 Squid Loligo vulgaris vertebrate mollusca 24.289099 PFDoDA 12 NA No Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C159 NA 40 1 ng/g 0.1000000 <LOD NA sd technical 40 1 1.0900000 NA 0.0200000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000
F013 Vassiliadou_2015 2015 Greece E612 Squid Loligo vulgaris vertebrate mollusca 24.289099 PFOS 8 linear Yes Grilling No liquid NA 180 NA No No NA NA 0.000 NA NA C159 NA 40 1 ng/g 0.1000000 <LOD NA sd technical 40 1 1.1900000 NA 0.1700000 technical 1 ng/g 0.2 0.59 Shared control Table 3 No Authors replied, we received liquid/fish ratio in personal communication from author NA NA NA 0.0000000

Import phylogenetic information and calculate phylogenetic variance-covariance matrix

The phylogenetic tree was generated in the tree_cooked_fish_MA.Rmd document

tree <- read.tree(here("data", "phylogenetic_tree.tre"))  # Import phylogenetic tree (see tree_cooked_fish_MA.Rmd for more details) 

tree <- compute.brlen(tree)  # Generate branch lengths 

cor_tree <- vcv(tree, corr = T)  # Generate phylogenetic variance-covariance matrix 

dat$Phylogeny <- str_replace(dat$Species_Scientific, " ", "_")  # Add the `phylogeny` column to the data frame

colnames(cor_tree) %in% dat$Phylogeny  # Check correspondence between tip names and data frame
##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
plot(tree)

Calculate effect sizes

The average coefficient of variation in PFAS concentration was calculated for each study and treatment, according to Doncaster and Spake (2018) Correction for bias in meta-analysis of little-replicated studies. Methods in Ecology and Evolution; 9:634-644. Then, these values were averaged across studies and used to calculate the lnRR corrected for small sample sizes (for formula, see the lnRR_func above)

aCV2 <- dat %>% 
               group_by(Study_ID) %>%  # Group by study 
                                     summarise(CV2c = mean((SDc/Mc)^2, na.rm = T),  # Calculate the squared coefficient of variation for control and experimental groups
                                               CV2e = mean((SDe/Me)^2, na.rm = T)) %>% 
                                                                                      ungroup() %>% # ungroup 
                                                                                                   summarise(aCV2c = mean(CV2c, na.rm = T), # Mean CV^2 for exp and control groups across studies
                                                                                                             aCV2e = mean(CV2e, na.rm = T)) 

effect <- lnRR_func(Mc = dat$Mc, 
                    Nc = dat$Nc, 
                    Me = dat$Me, 
                    Ne = dat$Ne, 
                    aCV2c = aCV2[[1]], 
                    aCV2e = aCV2[[2]],
                    rho = 0.5)  # Calculate effect sizes

dat <- dat %>% 
             mutate(N_tilde = (Nc*Ne)/(Nc + Ne)) # Calculate the effective sample size

dat <- cbind(dat, effect) # Merge effect sizes with the data frame

VCV_lnRR <- make_VCV_matrix(dat, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # Because some effect sizes share the same control, we generated a variance-covariance matrix to account for correlated errors (i.e. effectively dividing the weight of the correlated estimates by half)

Distribution of effect sizes

# mean
ggplot(dat, aes(x = lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.2) +
    theme_classic()

# variance
ggplot(dat, aes(x = var_lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) +
    theme_classic()

# log variance
ggplot(dat, aes(x = var_lnRR)) + geom_histogram(fill = "salmon", col = "black", binwidth = 0.05) +
    scale_x_log10() + theme_classic()

Sample sizes

Table of sample sizes

dat %>%
       summarise( # Calculate the number of effect sizes, studies and species for the main categorical variables
                 `Studies` = n_distinct(Study_ID),
                 `Species` = n_distinct(Species_common),
                 `PFAS type` = n_distinct(PFAS_type),
                 `Cohorts` = n_distinct(Cohort_ID),
                 `Effect sizes` = n_distinct(Effect_ID),
    
                 `Effect sizes (Oil-based)` = n_distinct(Effect_ID[Cooking_Category=="oil-based"]),
                 `Studies (Oil-based)` = n_distinct(Study_ID[Cooking_Category=="oil-based"]),
                 `Species (Oil-based)` = n_distinct(Species_common[Cooking_Category=="oil-based"]),

                 `Effect sizes (Water-based)` = n_distinct(Effect_ID[Cooking_Category=="water-based"]),
                 `Studies (Water-based)` = n_distinct(Study_ID[Cooking_Category=="water-based"]),
                 `Species (Water-based)` = n_distinct(Species_common[Cooking_Category=="water-based"]),

                 `Effect sizes (No liquid)` = n_distinct(Effect_ID[Cooking_Category=="No liquid"]),
                 `Studies (No liquid)` = n_distinct(Study_ID[Cooking_Category=="No liquid"]),
                 `Species (No liquid)` = n_distinct(Species_common[Cooking_Category=="No liquid"]),) -> table_sample_sizes

table_sample_sizes<-t(table_sample_sizes)
colnames(table_sample_sizes)<-"n (sample size)"
kable(table_sample_sizes) %>% kable_styling("striped", position="left")
n (sample size)
Studies 10
Species 39
PFAS type 18
Cohorts 153
Effect sizes 512
Effect sizes (Oil-based) 303
Studies (Oil-based) 7
Species (Oil-based) 28
Effect sizes (Water-based) 140
Studies (Water-based) 8
Species (Water-based) 23
Effect sizes (No liquid) 69
Studies (No liquid) 2
Species (No liquid) 14

Summary of the dataset

kable(summary(dat), "html") %>%
    kable_styling("striped", position = "left") %>%
    scroll_box(width = "100%", height = "500px")
Study_ID Author_year Publication_year Country_firstAuthor Effect_ID Species_common Species_Scientific Invertebrate_vertebrate Fish_mollusc Moisture_loss_in_percent PFAS_type PFAS_carbon_chain linear_total Choice_of_9 Cooking_method Cooking_Category Comments_cooking Temperature_in_Celsius Length_cooking_time_in_s Water Oil Oil_type Volume_liquid_ml Volume_liquid_ml_0 Ratio_liquid_fish Weigh_g_sample Cohort_ID Cohort_comment Nc Pooled_Nc Unit_PFAS_conc Mc Mc_comment Sc sd Sc_technical_biological Ne Pooled_Ne Me Me_comment Se Se_technical_biological If_technical_how_many Unit_LOD_LOQ LOD LOQ Design DataSource Raw_data_provided General_comments checked SDc SDe Phylogeny N_tilde lnRR var_lnRR
Length:512 Length:512 Min. :2008 Length:512 Length:512 Length:512 Length:512 Length:512 Length:512 Min. : 6.77 Length:512 Min. : 3.000 Length:512 Length:512 Length:512 Length:512 Length:512 Min. : 75.0 Min. : 120.0 Length:512 Length:512 Length:512 Min. : 0.341 Min. : 0.0 Min. : 0.00266 Min. : 10.0 Length:512 Length:512 Min. : 1.00 Min. :1.000 Length:512 Min. : 0.002 Length:512 Min. : 0.0010 Length:512 Length:512 Min. : 1.00 Min. :1.000 Min. : 0.0020 Length:512 Min. : 0.000 Length:512 Min. :1.000 Length:512 Length:512 Length:512 Length:512 Length:512 Length:512 Length:512 Length:512 Min. : 0.0010 Min. : 0.0010 Length:512 Min. : 0.500 Min. :-6.0350 Min. :0.01679
Class :character Class :character 1st Qu.:2014 Class :character Class :character Class :character Class :character Class :character Class :character 1st Qu.:14.45 Class :character 1st Qu.: 8.000 Class :character Class :character Class :character Class :character Class :character 1st Qu.:100.0 1st Qu.: 600.0 Class :character Class :character Class :character 1st Qu.: 11.000 1st Qu.: 5.0 1st Qu.: 0.10004 1st Qu.: 10.0 Class :character Class :character 1st Qu.: 5.00 1st Qu.:1.000 Class :character 1st Qu.: 0.160 Class :character 1st Qu.: 0.0010 Class :character Class :character 1st Qu.: 5.00 1st Qu.:1.000 1st Qu.: 0.0940 Class :character 1st Qu.: 0.001 Class :character 1st Qu.:1.000 Class :character Class :character Class :character Class :character Class :character Class :character Class :character Class :character 1st Qu.: 0.0354 1st Qu.: 0.0585 Class :character 1st Qu.: 2.500 1st Qu.:-0.8778 1st Qu.:0.08394
Mode :character Mode :character Median :2019 Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character Median :18.35 Mode :character Median : 8.000 Mode :character Mode :character Mode :character Mode :character Mode :character Median :160.0 Median : 600.0 Mode :character Mode :character Mode :character Median : 300.000 Median : 250.0 Median : 2.50000 Median : 70.0 Mode :character Mode :character Median :10.00 Median :1.000 Mode :character Median : 0.298 Mode :character Median : 0.0100 Mode :character Mode :character Median :10.00 Median :1.000 Median : 0.2285 Mode :character Median : 0.020 Mode :character Median :3.000 Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character Mode :character Median : 0.1580 Median : 0.1461 Mode :character Median : 5.000 Median :-0.1671 Median :0.08394
NA NA Mean :2017 NA NA NA NA NA NA Mean :21.04 NA Mean : 8.994 NA NA NA NA NA Mean :161.3 Mean : 733.3 NA NA NA Mean : 271.946 Mean : 231.8 Mean :13.58240 Mean : 149.1 NA NA Mean :10.34 Mean :2.316 NA Mean : 3.494 NA Mean : 1.7676 NA NA Mean :11.49 Mean :2.371 Mean : 3.2321 NA Mean : 1.822 NA Mean :2.481 NA NA NA NA NA NA NA NA Mean : 4.4069 Mean : 4.4491 NA Mean : 5.297 Mean :-0.3637 Mean :0.11794
NA NA 3rd Qu.:2019 NA NA NA NA NA NA 3rd Qu.:21.31 NA 3rd Qu.:11.000 NA NA NA NA NA 3rd Qu.:175.0 3rd Qu.: 900.0 NA NA NA 3rd Qu.: 300.000 3rd Qu.: 300.0 3rd Qu.:30.00000 3rd Qu.: 178.4 NA NA 3rd Qu.:10.00 3rd Qu.:5.000 NA 3rd Qu.: 1.083 NA 3rd Qu.: 0.1185 NA NA 3rd Qu.:10.00 3rd Qu.:5.000 3rd Qu.: 1.0505 NA 3rd Qu.: 0.130 NA 3rd Qu.:3.000 NA NA NA NA NA NA NA NA 3rd Qu.: 0.5600 3rd Qu.: 0.6516 NA 3rd Qu.: 5.000 3rd Qu.: 0.1849 3rd Qu.:0.16787
NA NA Max. :2020 NA NA NA NA NA NA Max. :79.11 NA Max. :14.000 NA NA NA NA NA Max. :300.0 Max. :1500.0 NA NA NA Max. :2500.000 Max. :2500.0 Max. :45.33092 Max. :1000.0 NA NA Max. :50.00 Max. :6.000 NA Max. :86.689 NA Max. :133.7000 NA NA Max. :60.00 Max. :6.000 Max. :134.4379 NA Max. :130.500 NA Max. :4.000 NA NA NA NA NA NA NA NA Max. :133.7000 Max. :130.5000 NA Max. :25.000 Max. : 3.4622 Max. :0.83936
NA NA NA NA NA NA NA NA NA NA’s :284 NA NA NA NA NA NA NA NA’s :6 NA’s :56 NA NA NA NA’s :114 NA’s :45 NA’s :88 NA’s :106 NA NA NA NA NA NA NA NA’s :53 NA NA NA NA NA NA NA’s :55 NA NA’s :198 NA NA NA NA NA NA NA NA NA’s :330 NA’s :328 NA NA NA NA

Intercept meta-analytical model

Determine the random effect structure

Cohort_ID explains virtually no variance in the model. Hence, it was removed from the model. All the other random effects explained significant variance and were kept in subsequent models

MA_all_rand_effects <- rma.mv(lnRR, VCV_lnRR, # Add `VCV_lnRR` to account for correlated errors errors between cohorts (shared_controls)
              random = list(~1|Study_ID, # Identity of the study
                            ~1|Phylogeny, # Phylogenetic correlation
                            ~1|Cohort_ID, # Identity of the cohort (shared controls)
                            ~1|Species_common, # Non-phylogenetic correlation between species
                            ~1|PFAS_type, # Type of PFAS 
                            ~1|Effect_ID), # Effect size identity 
              R= list(Phylogeny = cor_tree), # Assign the 'Phylogeny' argument to the phylogenetic variance-covariance matrix
              test = "t", 
              data = dat,
              sparse = TRUE)

summary(MA_all_rand_effects) # Cohort ID does not explain any variance 
## 
## Multivariate Meta-Analysis Model (k = 512; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -625.3701  1250.7402  1264.7402  1294.3947  1264.9628   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5837  0.7640     10     no        Study_ID   no 
## sigma^2.2  0.0000  0.0005     38     no       Phylogeny  yes 
## sigma^2.3  0.0000  0.0000    153     no       Cohort_ID   no 
## sigma^2.4  0.2222  0.4714     39     no  Species_common   no 
## sigma^2.5  0.0973  0.3119     18     no       PFAS_type   no 
## sigma^2.6  0.5003  0.7073    512     no       Effect_ID   no 
## 
## Test for Heterogeneity:
## Q(df = 511) = 11056.9620, p-val < .0001
## 
## Model Results:
## 
## estimate      se     tval   df    pval    ci.lb   ci.ub 
##  -0.3259  0.2856  -1.1413  511  0.2543  -0.8871  0.2352    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Intercept meta-analytical model and percentage of heterogeneity

MA_model <- rma.mv(lnRR, VCV_lnRR, 
              random = list(~1|Study_ID,
                            ~1|Phylogeny, # Removed Cohort_ID
                            ~1|Species_common, 
                            ~1|PFAS_type, 
                            ~1|Effect_ID), 
              R= list(Phylogeny = cor_tree), 
              test = "t", 
              data = dat,
               sparse = TRUE)

summary(MA_model)
## 
## Multivariate Meta-Analysis Model (k = 512; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -625.3701  1250.7402  1262.7402  1288.1584  1262.9068   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5837  0.7640     10     no        Study_ID   no 
## sigma^2.2  0.0000  0.0005     38     no       Phylogeny  yes 
## sigma^2.3  0.2222  0.4714     39     no  Species_common   no 
## sigma^2.4  0.0973  0.3119     18     no       PFAS_type   no 
## sigma^2.5  0.5003  0.7073    512     no       Effect_ID   no 
## 
## Test for Heterogeneity:
## Q(df = 511) = 11056.9620, p-val < .0001
## 
## Model Results:
## 
## estimate      se     tval   df    pval    ci.lb   ci.ub 
##  -0.3259  0.2856  -1.1413  511  0.2543  -0.8871  0.2352    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
round(i2_ml(MA_model)*100,2) # Percentage of heterogeneity explained by each random effect
##          I2_total       I2_Study_ID      I2_Phylogeny I2_Species_common 
##             94.62             39.35              0.00             14.98 
##      I2_PFAS_type      I2_Effect_ID 
##              6.56             33.73
# plot
orchard_plot(MA_model, mod = "Int", xlab = "lnRR", alpha=0.4) +  # Orchard plot 
           geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
           geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2)+ # confidence intervals
           geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
           scale_colour_manual(values = "darkorange")+ # change colours
           scale_fill_manual(values="darkorange")+ 
           scale_size_continuous(range = c(1, 7))+ # change point scaling
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 24), # change font sizes
                 legend.title = element_text(size = 15),
                 legend.text = element_text(size = 13)) 

save(MA_model, MA_all_rand_effects, file = here("Rdata", "int_MA_models.RData")) # save the models 

Single moderator meta-regressions

Function to run all models with the same structure

run_model<-function(data,formula){
  data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix 
  VCV<-make_VCV_matrix(data
                       , V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
  
  rma.mv(lnRR, VCV, # run the model, as described earlier
         mods=formula,
         random = list(~1|Study_ID,
                       ~1|Phylogeny, 
                       ~1|Species_common, 
                       ~1|PFAS_type, 
                       ~1|Effect_ID), 
         R= list(Phylogeny = cor_tree), 
         test = "t", 
         data = data,
         sparse=TRUE) # Make the model run faster
}

Function to run plots with the same structure

plot_continuous<-function(data, model, moderator, xlab){

pred<-predict.rma(model)

data %>% mutate(fit=pred$pred, 
               ci.lb=pred$ci.lb,
               ci.ub=pred$ci.ub,
               pr.lb=pred$cr.lb,
               pr.ub=pred$cr.ub) %>% # Add confidence intervals, mean predictions and prediction intervals
ggplot(aes(x = moderator, y = lnRR)) +
     geom_ribbon(aes(ymin = pr.lb, ymax = pr.ub, color = NULL), alpha = .075) + # Shaded area for prediction intervals
     geom_ribbon(aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = .2) + # Shaded area for confidence intervals
     geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) + # Points scaled by precision
     scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
     geom_line(aes(y = fit), size = 1.5)+  # Regression line
  labs(x = xlab, y = "lnRR", size = "Precison (1/SE)") +
  theme_bw() +
  scale_size_continuous(range=c(1,9))+ # Point scaling
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))
}

Single-moderator models

All continuous variables were z-transformed

Cooking time

# Length_cooking_time_in_s

time_model <- run_model(dat, ~scale(Length_cooking_time_in_s))  # z-transformed

summary(time_model)
## 
## Multivariate Meta-Analysis Model (k = 456; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -515.6836  1031.3672  1045.3672  1074.1939  1045.6183   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5316  0.7291      9     no        Study_ID   no 
## sigma^2.2  0.0000  0.0002     30     no       Phylogeny  yes 
## sigma^2.3  0.1715  0.4142     30     no  Species_common   no 
## sigma^2.4  0.0982  0.3133     17     no       PFAS_type   no 
## sigma^2.5  0.4092  0.6397    456     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 454) = 6658.0429, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 454) = 27.6416, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     tval   df    pval 
## intrcpt                           -0.5548  0.2875  -1.9301  454  0.0542 
## scale(Length_cooking_time_in_s)   -0.2567  0.0488  -5.2575  454  <.0001 
##                                    ci.lb    ci.ub 
## intrcpt                          -1.1197   0.0101    . 
## scale(Length_cooking_time_in_s)  -0.3526  -0.1607  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(time_model)  # Estimate R squared
##   R2_marginal R2_coditional 
##    0.05161056    0.67939517
# Plot
dat.time <- filter(dat, Length_cooking_time_in_s != "NA")  # Need to remove the NAs from the data
plot_continuous(dat.time, time_model, dat.time$Length_cooking_time_in_s, "Cooking time (s)")

Ratio between tissue sample and liquid volume, with dry cooking coded as NA

# Ratio_liquid_fish
dat <- dat %>%
    mutate(Ratio_liquid_fish_0 = ifelse(Cooking_Category == "No liquid", 0, Ratio_liquid_fish))  # Add a 0 when the cooking category is 'No liquid', otherwise keep the same value of Ratio_liquid_fish

volume_model <- run_model(dat, ~scale(log(Ratio_liquid_fish)))  # logged and z-transformed

summary(volume_model)
## 
## Multivariate Meta-Analysis Model (k = 424; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -531.2435  1062.4869  1076.4869  1104.8020  1076.7575   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5490  0.7410      8     no        Study_ID   no 
## sigma^2.2  0.0000  0.0001     34     no       Phylogeny  yes 
## sigma^2.3  0.1517  0.3895     35     no  Species_common   no 
## sigma^2.4  0.1126  0.3355     18     no       PFAS_type   no 
## sigma^2.5  0.5452  0.7384    424     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 422) = 8122.5881, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 422) = 4.1546, p-val = 0.0421
## 
## Model Results:
## 
##                                estimate      se     tval   df    pval    ci.lb 
## intrcpt                         -0.4422  0.2994  -1.4771  422  0.1404  -1.0308 
## scale(log(Ratio_liquid_fish))   -0.2578  0.1265  -2.0383  422  0.0421  -0.5064 
##                                  ci.ub 
## intrcpt                         0.1463    
## scale(log(Ratio_liquid_fish))  -0.0092  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(volume_model)
##   R2_marginal R2_coditional 
##    0.04664436    0.61740353
# Plot
dat.volume <- filter(dat, Ratio_liquid_fish != "NA")
plot_continuous(dat.volume, volume_model, log(dat.volume$Ratio_liquid_fish), "ln (Liquid volume to tissue sample ratio)")

Ratio between tissue sample and liquid volume, with dry cooking coded as 0

volume0_model <- run_model(dat, ~scale(log(Ratio_liquid_fish_0 + 1)))  # logged and z-transformed after adding 1

summary(volume0_model)
## 
## Multivariate Meta-Analysis Model (k = 493; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -596.7745  1193.5490  1207.5490  1236.9241  1207.7809   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.6069  0.7790      8     no        Study_ID   no 
## sigma^2.2  0.0000  0.0002     34     no       Phylogeny  yes 
## sigma^2.3  0.2128  0.4613     35     no  Species_common   no 
## sigma^2.4  0.1216  0.3487     18     no       PFAS_type   no 
## sigma^2.5  0.4910  0.7007    493     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 491) = 9498.4285, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 491) = 4.3306, p-val = 0.0380
## 
## Model Results:
## 
##                                      estimate      se     tval   df    pval 
## intrcpt                               -0.4388  0.3164  -1.3868  491  0.1661 
## scale(log(Ratio_liquid_fish_0 + 1))   -0.1156  0.0555  -2.0810  491  0.0380 
##                                        ci.lb    ci.ub 
## intrcpt                              -1.0604   0.1829    
## scale(log(Ratio_liquid_fish_0 + 1))  -0.2247  -0.0065  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(volume0_model)
##   R2_marginal R2_coditional 
##   0.009240908   0.660359049
# Plot
dat.volume0 <- filter(dat, Ratio_liquid_fish_0 != "NA")
plot_continuous(dat.volume0, volume0_model, log(dat.volume0$Ratio_liquid_fish_0 +
    1), "ln (Liquid volume to tissue sample ratio) + 1")

Cooking temperature

# Temperature_in_Celsius

temp_model <- run_model(dat, ~scale(Temperature_in_Celsius))  # z-transformed 

summary(temp_model)
## 
## Multivariate Meta-Analysis Model (k = 506; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -616.8140  1233.6280  1247.6280  1277.1861  1247.8538   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5782  0.7604     10     no        Study_ID   no 
## sigma^2.2  0.0000  0.0003     38     no       Phylogeny  yes 
## sigma^2.3  0.2202  0.4692     39     no  Species_common   no 
## sigma^2.4  0.0938  0.3062     18     no       PFAS_type   no 
## sigma^2.5  0.5018  0.7084    506     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 504) = 10706.4270, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 504) = 0.0242, p-val = 0.8765
## 
## Model Results:
## 
##                                estimate      se     tval   df    pval    ci.lb 
## intrcpt                         -0.3110  0.2861  -1.0870  504  0.2776  -0.8730 
## scale(Temperature_in_Celsius)    0.0112  0.0721   0.1555  504  0.8765  -0.1304 
##                                 ci.ub 
## intrcpt                        0.2511    
## scale(Temperature_in_Celsius)  0.1528    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(temp_model)
##   R2_marginal R2_coditional 
##  9.013051e-05  6.400389e-01
# Plot
dat.temp <- filter(dat, Temperature_in_Celsius != "NA")
plot_continuous(dat.temp, temp_model, dat.temp$Temperature_in_Celsius, "Cooking temperature")

PFAS carbon chain length

# PFAS_carbon_chain

PFAS_model <- run_model(dat, ~PFAS_carbon_chain)

summary(PFAS_model)
## 
## Multivariate Meta-Analysis Model (k = 512; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -623.8826  1247.7652  1261.7652  1291.4061  1261.9884   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5826  0.7633     10     no        Study_ID   no 
## sigma^2.2  0.0000  0.0010     38     no       Phylogeny  yes 
## sigma^2.3  0.2236  0.4728     39     no  Species_common   no 
## sigma^2.4  0.1016  0.3188     18     no       PFAS_type   no 
## sigma^2.5  0.5005  0.7075    512     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 510) = 10984.3510, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 0.1787, p-val = 0.6726
## 
## Model Results:
## 
##                    estimate      se     tval   df    pval    ci.lb   ci.ub 
## intrcpt             -0.4443  0.3994  -1.1124  510  0.2665  -1.2289  0.3403    
## PFAS_carbon_chain    0.0130  0.0308   0.4228  510  0.6726  -0.0474  0.0734    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(PFAS_model)
##   R2_marginal R2_coditional 
##  0.0006517908  0.6448264863
plot_continuous(dat, PFAS_model, dat$PFAS_carbon_chain, "PFAS carbon chain length")

Cooking category

# Cooking_Category

category_model<-run_model(dat, ~Cooking_Category-1)
  
summary(category_model)
## 
## Multivariate Meta-Analysis Model (k = 512; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -622.3146  1244.6292  1260.6292  1294.4888  1260.9172   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5872  0.7663     10     no        Study_ID   no 
## sigma^2.2  0.0000  0.0003     38     no       Phylogeny  yes 
## sigma^2.3  0.2237  0.4730     39     no  Species_common   no 
## sigma^2.4  0.0988  0.3144     18     no       PFAS_type   no 
## sigma^2.5  0.4996  0.7069    512     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 509) = 10895.4833, p-val < .0001
## 
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 509) = 1.2466, p-val = 0.2922
## 
## Model Results:
## 
##                              estimate      se     tval   df    pval    ci.lb 
## Cooking_CategoryNo liquid     -0.2055  0.3075  -0.6684  509  0.5042  -0.8097 
## Cooking_Categoryoil-based     -0.3843  0.2932  -1.3105  509  0.1906  -0.9603 
## Cooking_Categorywater-based   -0.2964  0.2915  -1.0169  509  0.3097  -0.8690 
##                               ci.ub 
## Cooking_CategoryNo liquid    0.3986    
## Cooking_Categoryoil-based    0.1918    
## Cooking_Categorywater-based  0.2762    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(category_model)
##   R2_marginal R2_coditional 
##    0.00290664    0.64651952
# plot
orchard_plot(category_model, mod = "Cooking_Category", xlab = "lnRR", alpha=0.4)+
           geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
           geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0, show.legend = FALSE, size = 2)+ # confidence intervals
           geom_point(aes(fill = name),  size = 5, shape = 21)+ # mean estimate
           scale_colour_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3"))+ # change colours
           scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+ 
           scale_size_continuous(range = c(1, 7))+ # change point scaling
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 24), # change font sizes
                 legend.title = element_text(size = 15),
                 legend.text = element_text(size = 13))

Percentage of moisture loss

This analysis is a posteriori and will only be presented in supplement.

# Moisture_loss_in_percent

moisture_model <- run_model(dat, ~scale(Moisture_loss_in_percent))

summary(moisture_model)
## 
## Multivariate Meta-Analysis Model (k = 228; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -223.7733   447.5465   461.5465   485.4903   462.0603   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.0802  0.2832      6     no        Study_ID   no 
## sigma^2.2  0.2262  0.4756     18     no       Phylogeny  yes 
## sigma^2.3  0.1339  0.3659     18     no  Species_common   no 
## sigma^2.4  0.0093  0.0965     17     no       PFAS_type   no 
## sigma^2.5  0.3105  0.5573    228     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 226) = 3369.7752, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 226) = 0.0866, p-val = 0.7688
## 
## Model Results:
## 
##                                  estimate      se     tval   df    pval 
## intrcpt                            0.5257  0.3278   1.6039  226  0.1101 
## scale(Moisture_loss_in_percent)   -0.0192  0.0654  -0.2943  226  0.7688 
##                                    ci.lb   ci.ub 
## intrcpt                          -0.1202  1.1717    
## scale(Moisture_loss_in_percent)  -0.1481  0.1096    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(moisture_model)
##   R2_marginal R2_coditional 
##  0.0004866973  0.5916335142
# Plot
dat.moisture <- filter(dat, Moisture_loss_in_percent != "NA")
plot_continuous(dat.moisture, moisture_model, dat.moisture$Moisture_loss_in_percent,
    "Percentage of moisture loss")

save(category_model, PFAS_model, temp_model, time_model, volume_model, volume0_model,
    moisture_model, file = here("Rdata", "single_mod_models.RData"))  # Save models

Full models

Full model with Ratio_liquid_fish as NA for the dry cooking category

# Testing cooking categories
full_model <- rma.mv(yi = lnRR, V = VCV_lnRR, mods = ~1 + Cooking_Category + scale(Temperature_in_Celsius) +
    scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)),
    random = list(~1 | Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type,
        ~1 | Effect_ID), R = list(Phylogeny = cor_tree), test = "t", data = dat,
    sparse = TRUE)
# btt = c(1:3)) # testing the significance of cooking category - testing first
# 3 regression coefficients)

summary(full_model)
## 
## Multivariate Meta-Analysis Model (k = 384; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -429.8045   859.6090   881.6090   924.8929   882.3303   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.4275  0.6538      7     no        Study_ID   no 
## sigma^2.2  0.2960  0.5441     26     no       Phylogeny  yes 
## sigma^2.3  0.0565  0.2376     26     no  Species_common   no 
## sigma^2.4  0.1225  0.3500     17     no       PFAS_type   no 
## sigma^2.5  0.4093  0.6397    384     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 378) = 5367.5620, p-val < .0001
## 
## Test of Moderators (coefficients 2:6):
## F(df1 = 5, df2 = 378) = 9.4240, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     tval   df    pval 
## intrcpt                           -0.7901  0.4174  -1.8930  378  0.0591 
## Cooking_Categoryoil-based          0.1177  0.1701   0.6921  378  0.4893 
## scale(Temperature_in_Celsius)     -0.3329  0.1310  -2.5406  378  0.0115 
## scale(Length_cooking_time_in_s)   -0.3298  0.0560  -5.8926  378  <.0001 
## scale(PFAS_carbon_chain)           0.0643  0.0798   0.8054  378  0.4211 
## scale(log(Ratio_liquid_fish))     -0.8149  0.1822  -4.4721  378  <.0001 
##                                    ci.lb    ci.ub 
## intrcpt                          -1.6108   0.0306    . 
## Cooking_Categoryoil-based        -0.2167   0.4522      
## scale(Temperature_in_Celsius)    -0.5905  -0.0753    * 
## scale(Length_cooking_time_in_s)  -0.4398  -0.2197  *** 
## scale(PFAS_carbon_chain)         -0.0927   0.2212      
## scale(log(Ratio_liquid_fish))    -1.1733  -0.4566  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model)
##   R2_marginal R2_coditional 
##     0.3178842     0.7871710
save(full_model, file = here("Rdata", "full_model.RData"))

Full model with Ratio_liquid_fish as 0 for the dry cooking category

full_model0 <- rma.mv(yi = lnRR, V = VCV_lnRR, mods = ~1 + Cooking_Category + scale(Temperature_in_Celsius) +
    scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish_0 +
    1)), random = list(~1 | Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type,
    ~1 | Effect_ID), R = list(Phylogeny = cor_tree), test = "t", data = dat, sparse = TRUE)
# btt = c(1:3)) # testing the significance of cooking category - testing first
# 3 regression coefficients)

summary(full_model0)
## 
## Multivariate Meta-Analysis Model (k = 431; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -454.8862   909.7723   933.7723   982.3691   934.5315   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.4384  0.6621      7     no        Study_ID   no 
## sigma^2.2  0.3862  0.6215     26     no       Phylogeny  yes 
## sigma^2.3  0.0391  0.1978     26     no  Species_common   no 
## sigma^2.4  0.1354  0.3680     17     no       PFAS_type   no 
## sigma^2.5  0.3572  0.5976    431     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 424) = 5465.2510, p-val < .0001
## 
## Test of Moderators (coefficients 2:7):
## F(df1 = 6, df2 = 424) = 12.1936, p-val < .0001
## 
## Model Results:
## 
##                                      estimate      se     tval   df    pval 
## intrcpt                               -2.3598  0.5351  -4.4097  424  <.0001 
## Cooking_Categoryoil-based              1.6600  0.3483   4.7661  424  <.0001 
## Cooking_Categorywater-based            1.9433  0.3898   4.9855  424  <.0001 
## scale(Temperature_in_Celsius)          0.0133  0.0976   0.1365  424  0.8915 
## scale(Length_cooking_time_in_s)       -0.3714  0.0497  -7.4679  424  <.0001 
## scale(PFAS_carbon_chain)               0.0657  0.0809   0.8123  424  0.4170 
## scale(log(Ratio_liquid_fish_0 + 1))   -0.8604  0.1541  -5.5829  424  <.0001 
##                                        ci.lb    ci.ub 
## intrcpt                              -3.4117  -1.3080  *** 
## Cooking_Categoryoil-based             0.9754   2.3446  *** 
## Cooking_Categorywater-based           1.1772   2.7095  *** 
## scale(Temperature_in_Celsius)        -0.1784   0.2051      
## scale(Length_cooking_time_in_s)      -0.4691  -0.2736  *** 
## scale(PFAS_carbon_chain)             -0.0933   0.2247      
## scale(log(Ratio_liquid_fish_0 + 1))  -1.1634  -0.5575  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model0)
##   R2_marginal R2_coditional 
##     0.4258950     0.8488249
save(full_model0, file = here("Rdata", "full_model.RData"))

Check collinearity of predictors

## Check for collinerarity - seems fine

vif(full_model)
## 
##       Cooking_Categoryoil-based   scale(Temperature_in_Celsius) 
##                          2.2542                          3.4186 
## scale(Length_cooking_time_in_s)        scale(PFAS_carbon_chain) 
##                          1.0493                          1.0000 
##   scale(log(Ratio_liquid_fish)) 
##                          1.8669
vif(full_model0)
## 
##           Cooking_Categoryoil-based         Cooking_Categorywater-based 
##                             14.3698                             14.2052 
##       scale(Temperature_in_Celsius)     scale(Length_cooking_time_in_s) 
##                              2.1185                              1.0724 
##            scale(PFAS_carbon_chain) scale(log(Ratio_liquid_fish_0 + 1)) 
##                              1.0000                              9.4572
dat %>%
    select(Temperature_in_Celsius, Length_cooking_time_in_s, PFAS_carbon_chain, Ratio_liquid_fish) %>%
    ggpairs()  # Estimate correlations between the variables

Conditional analyses

Inspection of the plots highlighted potential significant decreases in PFAS content with increased cooking time and volume of cooking. Hence, here we used emmeans (download from remotes::install_github(“rvlenth/emmeans”, dependencies = TRUE, build_opts = "")) to generate marginalised means at specified values of the different predictors. Such analysis enable the quantification of the mean effect size after controlling for different values of the moderators.

Note that these analyses were not performed separately using full models with Ratio_liquid_fish taken as NA or 0. Indeed, a full model containing the dry cooking category and the liquid ratio would extrapolate predictions for the dry cooking category at the mean liquid ratio; which is incorrect. Therefore, all full models were ran with the data containing NA for the Ratio_liquid_fish of the dry cooking method; and separate models were ran with a data subset only containing the dry cooking method.

Full model

# Full model in original units (no z-transformation)
dat$log_Ratio_liquid_fish <- log(dat$Ratio_liquid_fish)

full_model_org_units <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
    Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)



# Full model in original units (no z-transformation), but without the 'No
# liquid' data This model will be used for conditional analyses on the volume
# of liquid, where the data without liquid is irrelevant
dat_oil_water <- filter(dat, Cooking_Category != "No liquid")

full_model_org_units_oil_water <- run_model(dat_oil_water, ~-1 + Cooking_Category +
    Temperature_in_Celsius + Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)


# Full model in original units (no z-transformation), with Ratio_liquid_fish_0
dat$log_Ratio_liquid_fish0 <- log(dat$Ratio_liquid_fish_0 + 1)

full_model_org_units0 <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
    Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish0)



# Data subset only containing the data with the dry cooking method. Here, only
# the cooking time was added because the liquid ratio, cooking temperature, and
# PFAS carbon chain length do not have sufficient variability.

dat_dry <- filter(dat, Cooking_Category == "No liquid")
full_model_org_units_dry <- run_model(dat_dry, ~Length_cooking_time_in_s)


save(full_model_org_units, full_model_org_units_dry, full_model_org_units0, full_model_org_units_oil_water,
    file = here("Rdata", "full_models_org_units.RData"))

Overall marginalised mean

Overall mean with Ratio_liquid_fish taken as NA for the dry cooking category

res <- marginal_means(model = full_model_org_units, data = dat, mod = "1")
res$mod_table
##      name   estimate   lowerCL    upperCL   lowerPR  upperPR
## 1 Intrcpt -0.7572305 -1.556052 0.04159075 -3.146689 1.632228

Overall mean with Ratio_liquid_fish taken as 0 for the dry cooking category

res0 <- marginal_means(model = full_model_org_units0, data = dat, mod = "1")
res0$mod_table
##      name  estimate  lowerCL    upperCL   lowerPR  upperPR
## 1 Intrcpt -1.264438 -2.13653 -0.3923459 -3.714103 1.185226

Marginal means for different cooking categories

Overall mean for the water- and oil-based cooking

res_cat <- marginal_means(full_model_org_units, data = dat, mod = "1", by = "Cooking_Category")
res_cat$mod_table
##      name   condition   estimate   lowerCL     upperCL   lowerPR  upperPR
## 1 Intrcpt   oil-based -0.6983732 -1.508493 0.111746214 -3.091632 1.694886
## 2 Intrcpt water-based -0.8160879 -1.638197 0.006021405 -3.213432 1.581256
orchard_plot(res_cat, xlab = "lnRR", condition.lab = "Cooking Category")

Overall mean for the dry cooking

res_dry <- marginal_means(full_model_org_units_dry, data = dat, mod = "1")
res_dry$mod_table
##      name   estimate   lowerCL    upperCL   lowerPR   upperPR
## 1 Intrcpt -0.5699711 -1.000248 -0.1396944 -1.349651 0.2097082
orchard_plot(res_dry, xlab = "lnRR", condition.lab = "Cooking Category")

Marginal means for pre-determined cooking times

Here, we generate estimates at cooking times of 2, 10, and 25 min.

Marginal means for pre-determined cooking times, with Ratio_liquid_fish taken as NA for the dry cooking category

res_cooking_time <- marginal_means(full_model_org_units, data = dat, mod = "1", at = list(Length_cooking_time_in_s = c(120,
    600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time$mod_table
##      name condition    estimate    lowerCL    upperCL   lowerPR  upperPR
## 1 Intrcpt       120 -0.08017252 -0.8956537  0.7353087 -2.475252 2.314907
## 2 Intrcpt       600 -0.62181892 -1.4188934  0.1752556 -3.010694 1.767056
## 3 Intrcpt      1500 -1.63740593 -2.5067525 -0.7680593 -4.051357 0.776545
orchard_plot(res_cooking_time, xlab = "lnRR", condition.lab = "Cooking time (sec)")

Marginal means for pre-determined cooking times, with Ratio_liquid_fish taken as 0 for the dry cooking category

res_cooking_time0 <- marginal_means(full_model_org_units0, data = dat, mod = "1",
    at = list(Length_cooking_time_in_s = c(120, 600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time0$mod_table
##      name condition   estimate   lowerCL    upperCL   lowerPR   upperPR
## 1 Intrcpt       120 -0.4779287 -1.351086  0.3952287 -2.927973 1.9721153
## 2 Intrcpt       600 -1.0878893 -1.955934 -0.2198443 -3.536116 1.3603374
## 3 Intrcpt      1500 -2.2315654 -3.167381 -1.2957503 -4.704633 0.2415019
orchard_plot(res_cooking_time0, xlab = "lnRR", condition.lab = "Cooking time (sec)")

Marginalised means for the water- and oil-based cooking categories

res_cooking_time_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category",
    at = list(Length_cooking_time_in_s = c(120, 600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time_cat$mod_table
##          name condition    estimate    lowerCL    upperCL   lowerPR   upperPR
## 1   Oil-based       120 -0.02131516 -0.8514695  0.8088392 -2.421430 2.3787998
## 2 Water-based       120 -0.13902987 -0.9737688  0.6957091 -2.540734 2.2626747
## 3   Oil-based       600 -0.56296156 -1.3720966  0.2461735 -2.955888 1.8299645
## 4 Water-based       600 -0.68067628 -1.5003606  0.1390080 -3.077190 1.7158374
## 5   Oil-based      1500 -1.57854857 -2.4538669 -0.7032302 -3.994657 0.8375594
## 6 Water-based      1500 -1.69626328 -2.5914007 -0.8011259 -4.119622 0.7270952
orchard_plot(res_cooking_time_cat, xlab = "lnRR", condition.lab = "Cooking time (sec)")

Marginalised means for the dry cooking category

res_cooking_time_dry <- marginal_means(full_model_org_units_dry, data = dat, at = list(Length_cooking_time_in_s = c(120,
    600, 1500)), by = "Length_cooking_time_in_s")
res_cooking_time_dry$mod_table
##      name condition   estimate    lowerCL     upperCL    lowerPR    upperPR
## 1 Intrcpt       120  0.3317270 -0.2248785  0.88833238 -0.5241769  1.1876308
## 2 Intrcpt       600 -0.3552811 -0.8018081  0.09124584 -1.1440448  0.4334826
## 3 Intrcpt      1500 -1.6434213 -2.1519525 -1.13489008 -2.4688701 -0.8179724
orchard_plot(res_cooking_time_dry, xlab = "lnRR", condition.lab = "Cooking time (sec)")

Marginal means for different volumes of liquid

Ratio_liquid_fish taken as NA for the dry cooking category

Here, we generate marginalised estimates at volumes of liquid of ~0.1mL/g of tissue, ~10 ml/g of tissue, or 45 mL/g of tissue. We did not look at the means for different cooking categories because they are inherently different in the volume of liquid used. We also only used the data on oil and water because the “No liquid” category is not relevant for this analysis when considering Ratio_liquid_fish as NA.

res_volume <- marginal_means(full_model_org_units_oil_water, data = dat_oil_water,
    mod = "1", at = list(log_Ratio_liquid_fish = c(log(0.1), log(10), log(45))),
    by = "log_Ratio_liquid_fish")
res_volume$mod_table
##      name condition    estimate    lowerCL    upperCL   lowerPR   upperPR
## 1 Intrcpt -2.302585 -0.05967101 -0.9192784  0.7999364 -2.470132 2.3507896
## 2 Intrcpt  2.302585 -1.23336241 -2.0563665 -0.4103584 -3.631014 1.1642888
## 3 Intrcpt  3.806662 -1.61669737 -2.4956750 -0.7377197 -4.034133 0.8007387
orchard_plot(res_volume, xlab = "lnRR", condition.lab = "ln(Liquid volume to tissue sample ratio) (mL/g)")

Ratio_liquid_fish taken as 0 for the dry cooking category

Here, we generate marginalised estimates at volumes of liquid of 0mL/g of tissue (dry cooking), ~10 ml/g of tissue, or 45 mL/g of tissue.

res_volume0 <- marginal_means(full_model_org_units0, data = dat, mod = "1", at = list(log_Ratio_liquid_fish0 = c(0,
    log(10 + 1), log(45 + 1))), by = "log_Ratio_liquid_fish0")
res_volume0$mod_table
##      name condition   estimate   lowerCL    upperCL   lowerPR    upperPR
## 1 Intrcpt  0.000000 -0.3635577 -1.233235  0.5061198 -2.812364 2.08524832
## 2 Intrcpt  2.397895 -1.6778653 -2.588815 -0.7669157 -4.141631 0.78590033
## 3 Intrcpt  3.828641 -2.4620699 -3.500178 -1.4239621 -4.975629 0.05148929
orchard_plot(res_volume0, xlab = "lnRR", condition.lab = "ln(Liquid volume to tissue sample ratio) (mL/g)")

Marginal means for different PFAS carbon chains

Overall mean at the mean of each other predictor, with Ratio_liquid_fish taken as NA for the dry cooking category

Here, we generate marginalized estimates for PFAS of 3, 6, and 12 carbon chains

res_PFAS <- marginal_means(full_model_org_units, data = dat, mod = "1", at = list(PFAS_carbon_chain = c(3,
    6, 12)), by = "PFAS_carbon_chain")
res_PFAS$mod_table
##      name condition   estimate   lowerCL     upperCL   lowerPR  upperPR
## 1 Intrcpt         3 -0.9244650 -1.824179 -0.02475128 -3.349518 1.500588
## 2 Intrcpt         6 -0.8417098 -1.668156 -0.01526386 -3.240545 1.557125
## 3 Intrcpt        12 -0.6761994 -1.497777  0.14537834 -3.073361 1.720963
orchard_plot(res_PFAS, xlab = "lnRR", condition.lab = "PFAS carbon chain")

Overall mean at the mean of each other predictor, with Ratio_liquid_fish taken as 0 for the dry cooking category

res_PFAS0 <- marginal_means(full_model_org_units0, data = dat, mod = "1", at = list(PFAS_carbon_chain = c(3,
    6, 12)), by = "PFAS_carbon_chain")
res_PFAS0$mod_table
##      name condition  estimate   lowerCL    upperCL   lowerPR  upperPR
## 1 Intrcpt         3 -1.432143 -2.400325 -0.4639609 -3.917639 1.053352
## 2 Intrcpt         6 -1.347538 -2.245873 -0.4492031 -3.806668 1.111591
## 3 Intrcpt        12 -1.178328 -2.071472 -0.2851846 -3.635566 1.278910
orchard_plot(res_PFAS0, xlab = "lnRR", condition.lab = "PFAS carbon chain")

Marginalised mean estimate for the water- and oil-based cooking categories

res_PFAS_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category",
    at = list(PFAS_carbon_chain = c(3, 6, 12)), by = "PFAS_carbon_chain")
res_PFAS_cat$mod_table
##          name condition   estimate   lowerCL     upperCL   lowerPR  upperPR
## 1   Oil-based         3 -0.8656077 -1.775510  0.04429441 -3.294459 1.563243
## 2 Water-based         3 -0.9833224 -1.903634 -0.06301042 -3.416092 1.449448
## 3   Oil-based         6 -0.7828525 -1.620302  0.05459708 -3.185500 1.619796
## 4 Water-based         6 -0.9005672 -1.749467 -0.05166768 -3.307230 1.506096
## 5   Oil-based        12 -0.6173420 -1.449834  0.21514970 -3.018266 1.783582
## 6 Water-based        12 -0.7350567 -1.579369  0.10925582 -3.140105 1.669992
orchard_plot(res_PFAS_cat, xlab = "lnRR", condition.lab = "PFAS carbon chain")

Sub-group analyses for each cooking category

Here, we investigated whether the effect of the continuous moderators on lnRR vary depending on the cooking category. Hence, we performed subset analyses for each cooking category.

Oil-based cooking

Subset data and update function

oil_dat<-filter(dat, Cooking_Category=="oil-based")

include <- row.names(cor_tree) %in% oil_dat$Phylogeny # Check which rows are present in the phylogenetic tree 
cor_tree_oil <- cor_tree[include, include] # Only include the species that match the reduced data set 


run_model_oil<-function(data,formula){
  data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix 
  VCV<-make_VCV_matrix(data, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
  
  rma.mv(lnRR, VCV, # run the model, as described earlier
         mods=formula,
         random = list(~1|Study_ID,
                       ~1|Phylogeny, 
                       ~1|Species_common, 
                       ~1|PFAS_type, 
                       ~1|Effect_ID), 
         R= list(Phylogeny = cor_tree_oil), # cor_tree_oil here
         test = "t", 
         data = data)
}

Full model

full_model_oil <- run_model_oil(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
    scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)))

summary(full_model_oil)
## 
## Multivariate Meta-Analysis Model (k = 263; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -176.0279   352.0558   372.0558   407.5854   372.9465   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.1147  0.3387      6     no        Study_ID   no 
## sigma^2.2  0.0000  0.0000     19     no       Phylogeny  yes 
## sigma^2.3  0.0252  0.1586     19     no  Species_common   no 
## sigma^2.4  0.0485  0.2203     16     no       PFAS_type   no 
## sigma^2.5  0.1293  0.3596    263     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 258) = 1004.4883, p-val < .0001
## 
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 258) = 17.9272, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     tval   df    pval 
## intrcpt                           -0.5750  0.1811  -3.1746  258  0.0017 
## scale(Temperature_in_Celsius)     -0.0868  0.1173  -0.7404  258  0.4598 
## scale(Length_cooking_time_in_s)   -0.3782  0.0468  -8.0906  258  <.0001 
## scale(PFAS_carbon_chain)           0.1283  0.0613   2.0923  258  0.0374 
## scale(log(Ratio_liquid_fish))     -0.2048  0.2022  -1.0129  258  0.3121 
##                                    ci.lb    ci.ub 
## intrcpt                          -0.9317  -0.2183   ** 
## scale(Temperature_in_Celsius)    -0.3178   0.1441      
## scale(Length_cooking_time_in_s)  -0.4703  -0.2862  *** 
## scale(PFAS_carbon_chain)          0.0076   0.2491    * 
## scale(log(Ratio_liquid_fish))    -0.6030   0.1934      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_oil)
##   R2_marginal R2_coditional 
##     0.4421662     0.7730077
save(full_model_oil, file = here("Rdata", "full_model_oil.RData"))

Full model, with Ratio_liquid_fish taken as 0 for the dry cooking category

full_model_oil0 <- run_model_oil(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
    scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish_0 + 1)))

summary(full_model_oil0)
## 
## Multivariate Meta-Analysis Model (k = 263; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -174.9078   349.8156   369.8156   405.3452   370.7062   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.1110  0.3332      6     no        Study_ID   no 
## sigma^2.2  0.0000  0.0000     19     no       Phylogeny  yes 
## sigma^2.3  0.0225  0.1501     19     no  Species_common   no 
## sigma^2.4  0.0509  0.2257     16     no       PFAS_type   no 
## sigma^2.5  0.1287  0.3587    263     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 258) = 1001.1583, p-val < .0001
## 
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 258) = 18.4863, p-val < .0001
## 
## Model Results:
## 
##                                      estimate      se     tval   df    pval 
## intrcpt                               -0.5811  0.1787  -3.2522  258  0.0013 
## scale(Temperature_in_Celsius)         -0.0158  0.0786  -0.2012  258  0.8407 
## scale(Length_cooking_time_in_s)       -0.3791  0.0465  -8.1480  258  <.0001 
## scale(PFAS_carbon_chain)               0.1287  0.0621   2.0738  258  0.0391 
## scale(log(Ratio_liquid_fish_0 + 1))   -0.3162  0.1809  -1.7479  258  0.0817 
##                                        ci.lb    ci.ub 
## intrcpt                              -0.9330  -0.2293   ** 
## scale(Temperature_in_Celsius)        -0.1706   0.1390      
## scale(Length_cooking_time_in_s)      -0.4708  -0.2875  *** 
## scale(PFAS_carbon_chain)              0.0065   0.2509    * 
## scale(log(Ratio_liquid_fish_0 + 1))  -0.6724   0.0400    . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_oil0)
##   R2_marginal R2_coditional 
##     0.5608473     0.8195491
save(full_model_oil0, file = here("Rdata", "full_model_oil0.RData"))

Water-based cooking

Subset data and updating functions

water_dat<-filter(dat, Cooking_Category=="water-based")

include <- row.names(cor_tree) %in% water_dat$Phylogeny # Check which rows are present in the phylogenetic tree 
cor_tree_water <- cor_tree[include, include] # Only include the species that match the reduced data set 


run_model_water<-function(data,formula){
  data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix 
  VCV<-make_VCV_matrix(data, V = "var_lnRR", cluster = "Cohort_ID", obs = "Effect_ID", rho = 0.5) # create VCV matrix for the specified data
  
  rma.mv(lnRR, VCV, # run the model, as described earlier
         mods=formula,
         random = list(~1|Study_ID,
                       ~1|Phylogeny, 
                       ~1|Species_common, 
                       ~1|PFAS_type, 
                       ~1|Effect_ID), 
         R= list(Phylogeny = cor_tree_water), # cor_tree_water here
         test = "t", 
         data = data)
}

Full model

full_model_water <- run_model_water(water_dat, ~scale(Length_cooking_time_in_s) +
    scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)))

summary(full_model_water)
## 
## Multivariate Meta-Analysis Model (k = 121; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -178.5156   357.0312   375.0312   399.8908   376.7134   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5949  0.7713      6     no        Study_ID   no 
## sigma^2.2  0.0000  0.0002     19     no       Phylogeny  yes 
## sigma^2.3  0.0000  0.0000     19     no  Species_common   no 
## sigma^2.4  0.5412  0.7357     15     no       PFAS_type   no 
## sigma^2.5  0.9346  0.9667    121     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 117) = 4136.3260, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 117) = 4.2361, p-val = 0.0070
## 
## Model Results:
## 
##                                  estimate      se     tval   df    pval 
## intrcpt                           -1.3265  0.4166  -3.1844  117  0.0019 
## scale(Length_cooking_time_in_s)   -0.3738  0.1579  -2.3674  117  0.0196 
## scale(PFAS_carbon_chain)          -0.0488  0.1811  -0.2696  117  0.7879 
## scale(log(Ratio_liquid_fish))     -0.6521  0.2517  -2.5911  117  0.0108 
##                                    ci.lb    ci.ub 
## intrcpt                          -2.1515  -0.5015  ** 
## scale(Length_cooking_time_in_s)  -0.6865  -0.0611   * 
## scale(PFAS_carbon_chain)         -0.4075   0.3099     
## scale(log(Ratio_liquid_fish))    -1.1506  -0.1537   * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_water)
##   R2_marginal R2_coditional 
##     0.2135753     0.6450533

Full model, with Ratio_liquid_fish taken as 0 for the dry cooking category

full_model_water0 <- run_model_water(water_dat, ~scale(Length_cooking_time_in_s) +
    scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish_0 + 1)))

summary(full_model_water0)
## 
## Multivariate Meta-Analysis Model (k = 121; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -178.5072   357.0145   375.0145   399.8740   376.6967   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5855  0.7652      6     no        Study_ID   no 
## sigma^2.2  0.0000  0.0001     19     no       Phylogeny  yes 
## sigma^2.3  0.0000  0.0000     19     no  Species_common   no 
## sigma^2.4  0.5427  0.7367     15     no       PFAS_type   no 
## sigma^2.5  0.9342  0.9666    121     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 117) = 4133.3711, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 117) = 4.2697, p-val = 0.0067
## 
## Model Results:
## 
##                                      estimate      se     tval   df    pval 
## intrcpt                               -1.3203  0.4139  -3.1901  117  0.0018 
## scale(Length_cooking_time_in_s)       -0.3688  0.1577  -2.3388  117  0.0210 
## scale(PFAS_carbon_chain)              -0.0493  0.1813  -0.2719  117  0.7862 
## scale(log(Ratio_liquid_fish_0 + 1))   -0.6386  0.2446  -2.6109  117  0.0102 
##                                        ci.lb    ci.ub 
## intrcpt                              -2.1400  -0.5007  ** 
## scale(Length_cooking_time_in_s)      -0.6810  -0.0565   * 
## scale(PFAS_carbon_chain)             -0.4083   0.3097     
## scale(log(Ratio_liquid_fish_0 + 1))  -1.1230  -0.1542   * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_water0)
##   R2_marginal R2_coditional 
##     0.2094285     0.6418820

Comparison between steaming and other water-based cooking categories

In our data set, the studies using steaming-based cooking were considered to have an unknown (i.e. NA) because of the difficulty to assess how much liquid gets in contact with the products. Here, we provide an analysis to compare steaming with other water-based cooking categories

water_dat$steamed<-ifelse(water_dat$Cooking_method=="Steaming","steamed","other") # create a dummy variable to differentiate "steaming" with other types of water-based cooking

full_model_water_steamed <- run_model_water(water_dat, ~ -1 + # without intercept
                                                steamed +
                                                scale(Length_cooking_time_in_s) +
                                                scale(PFAS_carbon_chain)) # In this case, we need to remove the Ratio liquid fish from the model. Otherwise, it would remove observations where the liquid volume was unknown. 

summary(full_model_water_steamed)
## 
## Multivariate Meta-Analysis Model (k = 140; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -210.4341   420.8682   438.8682   465.0821   440.2968   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.6927  0.8323      8     no        Study_ID   no 
## sigma^2.2  0.0000  0.0001     23     no       Phylogeny  yes 
## sigma^2.3  0.0580  0.2409     23     no  Species_common   no 
## sigma^2.4  0.2654  0.5151     15     no       PFAS_type   no 
## sigma^2.5  0.9774  0.9886    140     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 136) = 4661.5782, p-val < .0001
## 
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 136) = 1.8805, p-val = 0.1174
## 
## Model Results:
## 
##                                  estimate      se     tval   df    pval 
## steamedother                      -0.7212  0.3858  -1.8691  136  0.0638 
## steamedsteamed                    -0.5578  0.4489  -1.2425  136  0.2162 
## scale(Length_cooking_time_in_s)   -0.3071  0.1590  -1.9316  136  0.0555 
## scale(PFAS_carbon_chain)          -0.0526  0.1409  -0.3731  136  0.7097 
##                                    ci.lb   ci.ub 
## steamedother                     -1.4842  0.0418  . 
## steamedsteamed                   -1.4455  0.3299    
## scale(Length_cooking_time_in_s)  -0.6216  0.0073  . 
## scale(PFAS_carbon_chain)         -0.3312  0.2261    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Contrast between steamed and non-steamed
full_model_water_steamed_cont <- run_model_water(water_dat,  
                                             ~  steamed + # with intercept
                                                scale(Length_cooking_time_in_s) +
                                                scale(PFAS_carbon_chain)) 

summary(full_model_water_steamed_cont)
## 
## Multivariate Meta-Analysis Model (k = 140; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -210.4341   420.8682   438.8682   465.0821   440.2968   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.6927  0.8323      8     no        Study_ID   no 
## sigma^2.2  0.0000  0.0001     23     no       Phylogeny  yes 
## sigma^2.3  0.0580  0.2409     23     no  Species_common   no 
## sigma^2.4  0.2654  0.5151     15     no       PFAS_type   no 
## sigma^2.5  0.9774  0.9886    140     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 136) = 4661.5782, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 136) = 1.3549, p-val = 0.2593
## 
## Model Results:
## 
##                                  estimate      se     tval   df    pval 
## intrcpt                           -0.7212  0.3858  -1.8691  136  0.0638 
## steamedsteamed                     0.1634  0.4029   0.4056  136  0.6856 
## scale(Length_cooking_time_in_s)   -0.3071  0.1590  -1.9316  136  0.0555 
## scale(PFAS_carbon_chain)          -0.0526  0.1409  -0.3731  136  0.7097 
##                                    ci.lb   ci.ub 
## intrcpt                          -1.4842  0.0418  . 
## steamedsteamed                   -0.6333  0.9602    
## scale(Length_cooking_time_in_s)  -0.6216  0.0073  . 
## scale(PFAS_carbon_chain)         -0.3312  0.2261    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
save(full_model_water, full_model_water_steamed, full_model_water_steamed_cont, file = here("Rdata", "full_model_water.RData"))

Dry cooking

Not very relevant because all effect sizes are from one study here. Also, the model does not converge when using VCV_lnRR

dry_dat<-filter(dat, Cooking_Category=="No liquid")

include <- row.names(cor_tree) %in% dry_dat$Phylogeny # Check which rows are present in the phylogenetic tree 
cor_tree_dry <- cor_tree[include, include] # Only include the species that match the reduced data set 


run_model_dry<-function(data,formula){
  data<-as.data.frame(data) # convert data set into a data frame to calculate VCV matrix 
  rma.mv(lnRR, var_lnRR, # run the model with var_lnRR instead of VCV
         mods=formula,
         random = list(~1|Study_ID,
                       ~1|Phylogeny, 
                       ~1|Species_common, 
                       ~1|PFAS_type, 
                       ~1|Effect_ID), 
         R= list(Phylogeny = cor_tree_dry), # cor_tree_dry here
         test = "t", 
         data = data)
}

Full model

full_model_dry <- run_model_dry(dry_dat, ~scale(Length_cooking_time_in_s))  # Model does not converge with VCV_lnRR

summary(full_model_dry)
## 
## Multivariate Meta-Analysis Model (k = 47; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -9.7496   19.4991   31.4991   42.3391   33.7096   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.0000  0.0000      1    yes        Study_ID   no 
## sigma^2.2  0.0038  0.0614      8     no       Phylogeny  yes 
## sigma^2.3  0.0125  0.1118      8     no  Species_common   no 
## sigma^2.4  0.0767  0.2769      2     no       PFAS_type   no 
## sigma^2.5  0.0000  0.0000     47     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 45) = 68.7069, p-val = 0.0130
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 45) = 64.9941, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     tval  df    pval    ci.lb 
## intrcpt                           -0.7776  0.2098  -3.7073  45  0.0006  -1.2001 
## scale(Length_cooking_time_in_s)   -0.3450  0.0428  -8.0619  45  <.0001  -0.4311 
##                                    ci.ub 
## intrcpt                          -0.3551  *** 
## scale(Length_cooking_time_in_s)  -0.2588  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
r2_ml(full_model_dry)
##   R2_marginal R2_coditional 
##     0.5614935     1.0000000
save(full_model_dry, file = here("Rdata", "full_model_dry.RData"))

Plots

Cooking time

Generate predictions

  oil_dat <- filter(dat, Cooking_Category=="oil-based")
  water_dat <- filter(dat, Cooking_Category=="water-based")
  dry_dat <- filter(dat, Cooking_Category=="No liquid")

  oil_dat_time<-filter(oil_dat, Length_cooking_time_in_s!="NA") 
  water_dat_time<-filter(water_dat, Length_cooking_time_in_s!="NA") 
  dry_dat_time<-filter(dry_dat, Length_cooking_time_in_s!="NA")
  
model_oil_time<-run_model_oil(oil_dat_time, ~Length_cooking_time_in_s) 
model_water_time<-run_model_water(water_dat_time, ~Length_cooking_time_in_s) 
model_dry_time<-run_model_dry(dry_dat_time, ~Length_cooking_time_in_s) 


pred_oil_time<-predict.rma(model_oil_time)
pred_water_time<-predict.rma(model_water_time)
pred_dry_time<-predict.rma(model_dry_time)

oil_dat_time<-mutate(oil_dat_time,
                    ci.lb = pred_oil_time$ci.lb, # lower bound of the confidence interval for oil
                    ci.ub = pred_oil_time$ci.ub, # upper bound of the confidence interval for oil
                    fit = pred_oil_time$pred) # regression line for oil

water_dat_time<-mutate(water_dat_time,
                    ci.lb = pred_water_time$ci.lb, # lower bound of the confidence interval for water
                    ci.ub = pred_water_time$ci.ub, # upper bound of the confidence interval for water
                    fit = pred_water_time$pred) # regression line for water

dry_dat_time<-mutate(dry_dat_time,
                    ci.lb = pred_dry_time$ci.lb, # lower bound of the confidence interval for dry
                    ci.ub = pred_dry_time$ci.ub, # upper bound of the confidence interval for dry
                    fit = pred_dry_time$pred) # regression line for dry

Plot

ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
  
       geom_ribbon(data=water_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=water_dat_time,aes(y = fit), size = 1.5, col="dodgerblue")+  
  
       geom_ribbon(data=oil_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=oil_dat_time,aes(y = fit), size = 1.5, col="goldenrod")+  
  
         geom_ribbon(data=dry_dat_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=dry_dat_time,aes(y = fit), size = 1.5, col="palegreen3")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Predictions with the full model, with Ratio_liquid_fish taken as NA for the dry cooking category

##### Oil based
full_model_oil_time<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Ratio_liquid_fish)))

pred_oil_time<-predict.rma(full_model_oil_time, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time<-as.data.frame(pred_oil_time)
pred_oil_time$Length_cooking_time_in_s=pred_oil_time$X.Length_cooking_time_in_s
pred_oil_time<-left_join(oil_dat, pred_oil_time, by="Length_cooking_time_in_s")


##### Water based
full_model_water_time<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Ratio_liquid_fish)))

pred_water_time<-predict.rma(full_model_water_time, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time<-as.data.frame(pred_water_time)
pred_water_time$Length_cooking_time_in_s=pred_water_time$X.Length_cooking_time_in_s
pred_water_time<-left_join(water_dat, pred_water_time, by="Length_cooking_time_in_s")

##### No liquid 

full_model_dry_time<- run_model_dry(dry_dat, ~ Length_cooking_time_in_s)

pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")




ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
  
       geom_ribbon(data=pred_water_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_time,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
       geom_ribbon(data=pred_oil_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_oil_time,aes(y = pred), size = 1.5, col="goldenrod")+  
  
        geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Predictions with the full model, with Ratio_liquid_fish taken as 0 for the dry cooking category

##### Oil based
full_model_oil_time0<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Ratio_liquid_fish_0 + 1)))

pred_oil_time0<-predict.rma(full_model_oil_time0, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time0<-as.data.frame(pred_oil_time0)
pred_oil_time0$Length_cooking_time_in_s=pred_oil_time0$X.Length_cooking_time_in_s
pred_oil_time0<-left_join(oil_dat, pred_oil_time0, by="Length_cooking_time_in_s")


##### Water based
full_model_water_time0<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Ratio_liquid_fish_0 + 1)))

pred_water_time0<-predict.rma(full_model_water_time0, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time0<-as.data.frame(pred_water_time0)
pred_water_time0$Length_cooking_time_in_s=pred_water_time0$X.Length_cooking_time_in_s
pred_water_time0<-left_join(water_dat, pred_water_time0, by="Length_cooking_time_in_s")

##### No liquid 

full_model_dry_time<- run_model_dry(dry_dat, ~ Length_cooking_time_in_s)

pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")




ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
  
       geom_ribbon(data=pred_water_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_time0,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
       geom_ribbon(data=pred_oil_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_oil_time0,aes(y = pred), size = 1.5, col="goldenrod")+  
  
        geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Liquid volume to tissue sample ratio

Single moderator models

oil_dat_vol <- filter(oil_dat, Ratio_liquid_fish != "NA")
water_dat_vol <- filter(water_dat, Ratio_liquid_fish != "NA")

model_oil_vol <- run_model_oil(oil_dat_vol, ~log(Ratio_liquid_fish))
model_water_vol <- run_model_water(water_dat_vol, ~log(Ratio_liquid_fish))


pred_oil_vol <- predict.rma(model_oil_vol)
pred_water_vol <- predict.rma(model_water_vol)

oil_dat_vol <- mutate(oil_dat_vol, ci.lb = pred_oil_vol$ci.lb, ci.ub = pred_oil_vol$ci.ub,
    fit = pred_oil_vol$pred)

water_dat_vol <- mutate(water_dat_vol, ci.lb = pred_water_vol$ci.lb, ci.ub = pred_water_vol$ci.ub,
    fit = pred_water_vol$pred)

oil_dat$log_Ratio_liquid_fish <- log(oil_dat$Ratio_liquid_fish)
water_dat$log_Ratio_liquid_fish <- log(water_dat$Ratio_liquid_fish)

Plot

ggplot(dat, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +

geom_ribbon(data = water_dat_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.2) + geom_line(data = water_dat_vol, aes(y = fit), size = 1.5, col = "dodgerblue") +

geom_ribbon(data = oil_dat_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
    geom_line(data = oil_dat_vol, aes(y = fit), size = 1.5, col = "goldenrod") +

geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue ratio (mL/g))",
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
        legend.direction = "horizontal", legend.title = element_text(size = 15),
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))

Predictions with the full model

##### Oil based
full_model_oil_vol <- run_model_oil(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
    scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_oil_vol <- predict.rma(full_model_oil_vol, addx = TRUE, newmods = cbind(0, 0,
    0, oil_dat$log_Ratio_liquid_fish))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_oil_vol <- as.data.frame(pred_oil_vol)
pred_oil_vol <- pred_oil_vol %>%
    mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "oil-based",
        lnRR = 0)  # for the plot to work, we need to add a column with cooking category and a column with lnRR


##### Water based

full_model_water_vol <- run_model_water(water_dat, ~scale(Temperature_in_Celsius) +
    scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)

pred_water_vol <- predict.rma(full_model_water_vol, addx = TRUE, newmods = cbind(0,
    0, water_dat$log_Ratio_liquid_fish))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_water_vol <- as.data.frame(pred_water_vol)
pred_water_vol <- pred_water_vol %>%
    mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "water-based",
        lnRR = 0)



ggplot(dat, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +

geom_ribbon(data = pred_water_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.2) + geom_line(data = pred_water_vol, aes(y = pred), size = 1.5, col = "dodgerblue") +

geom_ribbon(data = pred_oil_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
    geom_line(data = pred_oil_vol, aes(y = pred), size = 1.5, col = "goldenrod") +


geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio (mL/g))",
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
        legend.direction = "horizontal", legend.title = element_text(size = 15),
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))  #### The line doesn't go all the way down for water-based because the highest values are not included in the full model

PFAS carbon chain length

Generate predictions with single moderator models

oil_dat_PFAS <- filter(oil_dat, PFAS_carbon_chain != "NA")
water_dat_PFAS <- filter(water_dat, PFAS_carbon_chain != "NA")
dry_dat_PFAS <- filter(dry_dat, PFAS_carbon_chain != "NA")

model_oil_PFAS <- run_model_oil(oil_dat_PFAS, ~PFAS_carbon_chain)
model_water_PFAS <- run_model_water(water_dat_PFAS, ~PFAS_carbon_chain)
model_dry_PFAS <- run_model_dry(dry_dat_PFAS, ~PFAS_carbon_chain)


pred_oil_PFAS <- predict.rma(model_oil_PFAS)
pred_water_PFAS <- predict.rma(model_water_PFAS)
pred_dry_PFAS <- predict.rma(model_dry_PFAS)

oil_dat_PFAS <- mutate(oil_dat_PFAS, ci.lb = pred_oil_PFAS$ci.lb, ci.ub = pred_oil_PFAS$ci.ub,
    fit = pred_oil_PFAS$pred)

water_dat_PFAS <- mutate(water_dat_PFAS, ci.lb = pred_water_PFAS$ci.lb, ci.ub = pred_water_PFAS$ci.ub,
    fit = pred_water_PFAS$pred)

dry_dat_PFAS <- mutate(dry_dat_PFAS, ci.lb = pred_dry_PFAS$ci.lb, ci.ub = pred_dry_PFAS$ci.ub,
    fit = pred_dry_PFAS$pred)

Plot

For some reason the plot doesn’t want to knit, although the script works

ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +  ggplot(dat,
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +  aes(x
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +  =
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +  PFAS_carbon_chain,
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +  y
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +  =
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +  lnRR,
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +  fill
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +  =
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +  Cooking_Category))
ggplot(dat, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +  +
geom_ribbon(data = dry_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
    geom_line(data = dry_dat_PFAS, aes(y = fit), size = 1.5, col = "palegreen3") +
geom_ribbon(data = water_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.2) + geom_line(data = water_dat_PFAS, aes(y = fit), size = 1.5, col = "dodgerblue") +
geom_ribbon(data = oil_dat_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
    geom_line(data = oil_dat_PFAS, aes(y = fit), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "PFAS carbon chain length",
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
        legend.direction = "horizontal", legend.title = element_text(size = 15),
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))

Predictions with the full model, with Ratio_liquid_fish taken as NA for the dry cooking category

##### Oil based
full_model_oil_PFAS<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Ratio_liquid_fish)))
pred_oil_PFAS<-predict.rma(full_model_oil_PFAS, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS<-as.data.frame(pred_oil_PFAS)
pred_oil_PFAS$PFAS_carbon_chain=pred_oil_PFAS$X.PFAS_carbon_chain
pred_oil_PFAS<-left_join(oil_dat, pred_oil_PFAS, by="PFAS_carbon_chain")


##### Water based
full_model_water_PFAS<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Ratio_liquid_fish)))

pred_water_PFAS<-predict.rma(full_model_water_PFAS, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS<-as.data.frame(pred_water_PFAS)
pred_water_PFAS$PFAS_carbon_chain=pred_water_PFAS$X.PFAS_carbon_chain
pred_water_PFAS<-left_join(water_dat, pred_water_PFAS, by="PFAS_carbon_chain")

##### No liquid 

full_model_dry_PFAS<- run_model_dry(dry_dat, ~ PFAS_carbon_chain)

pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")




ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
  
    
       geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_ribbon(data=pred_water_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_PFAS,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
  
       geom_ribbon(data=pred_oil_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=pred_oil_PFAS,aes(y = pred), size = 1.5, col="goldenrod")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Predictions with the full model, with Ratio_liquid_fish taken as NA for the dry cooking category

##### Oil based
full_model_oil_PFAS0<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Ratio_liquid_fish_0 + 1)))
pred_oil_PFAS0<-predict.rma(full_model_oil_PFAS0, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS0<-as.data.frame(pred_oil_PFAS0)
pred_oil_PFAS0$PFAS_carbon_chain=pred_oil_PFAS0$X.PFAS_carbon_chain
pred_oil_PFAS0<-left_join(oil_dat, pred_oil_PFAS0, by="PFAS_carbon_chain")


##### Water based
full_model_water_PFAS0<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Ratio_liquid_fish + 1)))

pred_water_PFAS0<-predict.rma(full_model_water_PFAS0, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS0<-as.data.frame(pred_water_PFAS0)
pred_water_PFAS0$PFAS_carbon_chain=pred_water_PFAS0$X.PFAS_carbon_chain
pred_water_PFAS0<-left_join(water_dat, pred_water_PFAS0, by="PFAS_carbon_chain")

##### No liquid 

full_model_dry_PFAS<- run_model_dry(dry_dat, ~ PFAS_carbon_chain)

pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")




ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
  
    
       geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_ribbon(data=pred_water_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_PFAS0,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
  
       geom_ribbon(data=pred_oil_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=pred_oil_PFAS0,aes(y = pred), size = 1.5, col="goldenrod")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Publication bias

Funnel plot

funnel(full_model, yaxis = "seinv")

funnel(full_model)

Egger regressions

Full model with Ratio_liquid_fish taken as NA for the dry cooking category

egger_all <- run_model(dat, ~-1 + Cooking_Category + I(sqrt(1/N_tilde)) + scale(Publication_year) +
    scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
    scale(log(Ratio_liquid_fish)))
summary(egger_all)
## 
## Multivariate Meta-Analysis Model (k = 384; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -423.1839   846.3678   872.3678   923.4525   873.3733   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.0238  0.1543      7     no        Study_ID   no 
## sigma^2.2  0.0000  0.0010     26     no       Phylogeny  yes 
## sigma^2.3  0.1732  0.4161     26     no  Species_common   no 
## sigma^2.4  0.1239  0.3520     17     no       PFAS_type   no 
## sigma^2.5  0.4092  0.6397    384     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 376) = 4942.6818, p-val < .0001
## 
## Test of Moderators (coefficients 1:8):
## F(df1 = 8, df2 = 376) = 13.8754, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     tval   df    pval 
## Cooking_Categoryoil-based         -0.6366  0.3663  -1.7380  376  0.0830 
## Cooking_Categorywater-based       -0.7885  0.3655  -2.1574  376  0.0316 
## I(sqrt(1/N_tilde))                 0.0380  0.5939   0.0639  376  0.9491 
## scale(Publication_year)            0.4304  0.0903   4.7662  376  <.0001 
## scale(Temperature_in_Celsius)     -0.3562  0.1231  -2.8931  376  0.0040 
## scale(Length_cooking_time_in_s)   -0.3340  0.0533  -6.2618  376  <.0001 
## scale(PFAS_carbon_chain)           0.0813  0.0800   1.0168  376  0.3099 
## scale(log(Ratio_liquid_fish))     -0.9229  0.1498  -6.1629  376  <.0001 
##                                    ci.lb    ci.ub 
## Cooking_Categoryoil-based        -1.3568   0.0836    . 
## Cooking_Categorywater-based      -1.5072  -0.0699    * 
## I(sqrt(1/N_tilde))               -1.1297   1.2057      
## scale(Publication_year)           0.2528   0.6079  *** 
## scale(Temperature_in_Celsius)    -0.5983  -0.1141   ** 
## scale(Length_cooking_time_in_s)  -0.4389  -0.2291  *** 
## scale(PFAS_carbon_chain)         -0.0759   0.2385      
## scale(log(Ratio_liquid_fish))    -1.2174  -0.6285  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
funnel(egger_all, yaxis = "seinv")

funnel(egger_all)

# funnel(egger_all, yaxis = 'seinv') little evidence
egger_n <- run_model(dat, ~I(sqrt(1/N_tilde)))
summary(egger_n)
## 
## Multivariate Meta-Analysis Model (k = 512; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -623.6127  1247.2255  1261.2255  1290.8664  1261.4486   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5953  0.7715     10     no        Study_ID   no 
## sigma^2.2  0.0000  0.0004     38     no       Phylogeny  yes 
## sigma^2.3  0.2191  0.4680     39     no  Species_common   no 
## sigma^2.4  0.0973  0.3120     18     no       PFAS_type   no 
## sigma^2.5  0.5011  0.7079    512     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 510) = 10646.5573, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 0.1075, p-val = 0.7431
## 
## Model Results:
## 
##                     estimate      se     tval   df    pval    ci.lb   ci.ub 
## intrcpt              -0.2340  0.4017  -0.5824  510  0.5606  -1.0232  0.5553    
## I(sqrt(1/N_tilde))   -0.1995  0.6083  -0.3279  510  0.7431  -1.3946  0.9956    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Full model with Ratio_liquid_fish taken as NA for the dry cooking category

egger_all0 <- run_model(dat, ~-1 + Cooking_Category + I(sqrt(1/N_tilde)) + scale(Publication_year) +
    scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) +
    scale(log(Ratio_liquid_fish_0 + 1)))
summary(egger_all0)
## 
## Multivariate Meta-Analysis Model (k = 431; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -449.8422   899.6843   927.6843   984.3144   928.7163   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.1210  0.3479      7     no        Study_ID   no 
## sigma^2.2  0.0000  0.0001     26     no       Phylogeny  yes 
## sigma^2.3  0.1677  0.4095     26     no  Species_common   no 
## sigma^2.4  0.1397  0.3738     17     no       PFAS_type   no 
## sigma^2.5  0.3591  0.5992    431     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 422) = 5071.6337, p-val < .0001
## 
## Test of Moderators (coefficients 1:9):
## F(df1 = 9, df2 = 422) = 11.0177, p-val < .0001
## 
## Model Results:
## 
##                                      estimate      se     tval   df    pval 
## Cooking_CategoryNo liquid             -2.2926  0.4486  -5.1107  422  <.0001 
## Cooking_Categoryoil-based             -0.6732  0.3833  -1.7565  422  0.0797 
## Cooking_Categorywater-based           -0.4139  0.3942  -1.0501  422  0.2943 
## I(sqrt(1/N_tilde))                    -0.0426  0.5892  -0.0724  422  0.9423 
## scale(Publication_year)                0.3575  0.1263   2.8302  422  0.0049 
## scale(Temperature_in_Celsius)          0.0081  0.0966   0.0834  422  0.9335 
## scale(Length_cooking_time_in_s)       -0.3688  0.0490  -7.5336  422  <.0001 
## scale(PFAS_carbon_chain)               0.0750  0.0818   0.9168  422  0.3598 
## scale(log(Ratio_liquid_fish_0 + 1))   -0.8403  0.1354  -6.2068  422  <.0001 
##                                        ci.lb    ci.ub 
## Cooking_CategoryNo liquid            -3.1743  -1.4108  *** 
## Cooking_Categoryoil-based            -1.4266   0.0801    . 
## Cooking_Categorywater-based          -1.1887   0.3608      
## I(sqrt(1/N_tilde))                   -1.2008   1.1155      
## scale(Publication_year)               0.1092   0.6057   ** 
## scale(Temperature_in_Celsius)        -0.1819   0.1980      
## scale(Length_cooking_time_in_s)      -0.4650  -0.2726  *** 
## scale(PFAS_carbon_chain)             -0.0858   0.2358      
## scale(log(Ratio_liquid_fish_0 + 1))  -1.1064  -0.5742  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
funnel(egger_all0, yaxis = "seinv")

funnel(egger_all0)

# funnel(egger_all, yaxis = 'seinv') little evidence

save(egger_all, egger_all0, egger_n, file = here("Rdata", "egger_regressions.RData"))

Publication year

pub_year <- run_model(dat, ~Publication_year)
summary(pub_year)
## 
## Multivariate Meta-Analysis Model (k = 512; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -622.1910  1244.3820  1258.3820  1288.0229  1258.6051   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5573  0.7465     10     no        Study_ID   no 
## sigma^2.2  0.0026  0.0508     38     no       Phylogeny  yes 
## sigma^2.3  0.2199  0.4689     39     no  Species_common   no 
## sigma^2.4  0.0977  0.3126     18     no       PFAS_type   no 
## sigma^2.5  0.5004  0.7074    512     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 510) = 11056.9127, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 510) = 1.2825, p-val = 0.2580
## 
## Model Results:
## 
##                    estimate        se     tval   df    pval      ci.lb 
## intrcpt           -165.3375  145.7116  -1.1347  510  0.2570  -451.6063 
## Publication_year     0.0818    0.0723   1.1325  510  0.2580    -0.0601 
##                      ci.ub 
## intrcpt           120.9312    
## Publication_year    0.2238    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat, pub_year, dat$Publication_year, "Publication year")

##

Sensitivity analyses

Leave-one-out analyses

Here, we iteratively removed one study at the time and investigated how it affects the overall mean. Removing one of the study particularly modifies the estimate, but none of these models show a significant overall difference in PFAS concentration with cooking.

dat$Study_ID<-as.factor(dat$Study_ID)
dat<-as.data.frame(dat) # Only work with a dataframe
VCV_matrix<-list() # will need new VCV matrices because the sample size will be iteratively reduced
Leave1studyout<-list() # create a list that will host the results of each model 
for(i in 1:length(levels(dat$Study_ID))){ # N models = N studies 
  VCV_matrix[[i]]<-make_VCV_matrix(dat[dat$Study_ID != levels(dat$Study_ID)[i], ], V="var_lnRR", cluster="Cohort_ID", obs="Effect_ID") # Create a new VCV matrix for each new model
  Leave1studyout[[i]] <- rma.mv(yi = lnRR, V = VCV_matrix[[i]], # Same model structure as all the models we fitted
                                random = list(~1|Study_ID,
                                              ~1|Phylogeny, 
                                              ~1|Species_common, 
                                              ~1|PFAS_type, 
                                              ~1|Effect_ID),
                                R= list(Phylogeny = cor_tree), 
                                test = "t", 
                                data = dat[dat$Study_ID != levels(dat$Study_ID)[i], ]) # Generate a new model for each new data (iterative removal of one study at a time)
}

# The output is a list so we need to summarise the coefficients of all the models performed

results.Leave1studyout<-as.data.frame(cbind(
                                           sapply(Leave1studyout, function(x) summary(x)$beta), # extract the beta coefficient from all models
                                           sapply(Leave1studyout, function(x) summary(x)$se), # extract the standard error from all models
                                           sapply(Leave1studyout, function(x) summary(x)$zval),  # extract the z value from all models
                                           sapply(Leave1studyout, function(x) summary(x)$pval), # extract the p value from all models
                                           sapply(Leave1studyout, function(x) summary(x)$ci.lb), # extract the lower confidence interval for all models
                                           sapply(Leave1studyout, function(x) summary(x)$ci.ub))) # extract the upper confidence interval for all models

colnames(results.Leave1studyout)=c("Estimate", "SE", "zval", "pval", "ci.lb", "ci.ub") # change column names 
kable(results.Leave1studyout)%>% kable_styling("striped", position="left") %>% scroll_box(width="100%", height="500px") # Table of the results from all models
Estimate SE zval pval ci.lb ci.ub
-0.3358200 0.3051501 -1.1005074 0.2716313 -0.9353287 0.2636888
-0.4080024 0.3076228 -1.3263074 0.1853410 -1.0123891 0.1963844
-0.4142946 0.3406963 -1.2160233 0.2247270 -1.0841695 0.2555802
0.0222058 0.2673637 0.0830547 0.9338424 -0.5031283 0.5475399
-0.3372414 0.3106935 -1.0854474 0.2782695 -0.9477320 0.2732491
-0.2492592 0.2988556 -0.8340453 0.4046643 -0.8364648 0.3379465
-0.3419289 0.3089696 -1.1066751 0.2689687 -0.9489735 0.2651156
-0.2278845 0.3065464 -0.7433932 0.4577908 -0.8309927 0.3752236
-0.3927011 0.3175715 -1.2365753 0.2168470 -1.0166967 0.2312945
-0.4892206 0.2852553 -1.7150270 0.0870211 -1.0498019 0.0713607
dat %>% group_by(Author_year, Study_ID) %>% summarise(mean=mean(lnRR)) # Study F005 (DelGobbo_2008) has much lower effect sizes than the others. 
## # A tibble: 10 x 3
## # Groups:   Author_year [10]
##    Author_year      Study_ID    mean
##    <chr>            <fct>      <dbl>
##  1 Alves_2017       F001     -0.0774
##  2 Barbosa_2018     F002      0.198 
##  3 Bhavsar_2014     F003      0.153 
##  4 DelGobbo_2008    F005     -2.00  
##  5 Hu_2020          F006     -0.134 
##  6 Kim_2020         F007     -0.887 
##  7 Luo_2019         F008     -0.161 
##  8 Sungur_2019      F010     -0.893 
##  9 Taylor_2019      F011      0.208 
## 10 Vassiliadou_2015 F013      0.671

Subset analysis without Study_ID F005 (Del Gobbo et al. 2008)

Cooking time

dat.sens <- filter(dat, Author_year != "DelGobbo_2008")

include <- row.names(cor_tree) %in% dat.sens$Phylogeny  # Check which rows are present in the phylogenetic tree 
cor_tree_sens <- cor_tree[include, include]  # Only include the species that match the reduced data set 

dat.sens <- as.data.frame(dat.sens)  # convert data set into a data frame to calculate VCV matrix 
VCV_lnRR.sens <- make_VCV_matrix(dat.sens, V = "var_lnRR", cluster = "Cohort_ID",
    obs = "Effect_ID", rho = 0.5)  # create VCV matrix for the specified data

mod.sens <- rma.mv(lnRR, VCV_lnRR.sens, mods = ~Length_cooking_time_in_s, random = list(~1 |
    Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
    R = list(Phylogeny = cor_tree_sens), test = "t", data = dat.sens)
summary(mod.sens)
## 
## Multivariate Meta-Analysis Model (k = 430; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -261.7947   523.5893   537.5893   566.0032   537.8560   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.1970  0.4439      8     no        Study_ID   no 
## sigma^2.2  0.0194  0.1391     22     no       Phylogeny  yes 
## sigma^2.3  0.0159  0.1262     22     no  Species_common   no 
## sigma^2.4  0.0948  0.3079     17     no       PFAS_type   no 
## sigma^2.5  0.0882  0.2970    430     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 428) = 2086.8907, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 428) = 105.5222, p-val < .0001
## 
## Model Results:
## 
##                           estimate      se      tval   df    pval    ci.lb 
## intrcpt                     0.6221  0.2188    2.8434  428  0.0047   0.1920 
## Length_cooking_time_in_s   -0.0012  0.0001  -10.2724  428  <.0001  -0.0014 
##                             ci.ub 
## intrcpt                    1.0521   ** 
## Length_cooking_time_in_s  -0.0009  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dat.time.sens <- filter(dat.sens, Length_cooking_time_in_s != "NA")
plot_continuous(dat.time.sens, mod.sens, dat.time.sens$Length_cooking_time_in_s,
    "Cooking time (s)")  # The relationship with cooking time appears even stronger

Effect of cooking time on lnRR for each cooking category
oil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
dry_dat.sens <- filter(dat.sens, Cooking_Category == "No liquid")


oil_dat_time.sens <- filter(oil_dat.sens, Length_cooking_time_in_s != "NA")
water_dat_time.sens <- filter(water_dat.sens, Length_cooking_time_in_s != "NA")
dry_dat_time.sens <- filter(dry_dat.sens, Length_cooking_time_in_s != "NA")

model_oil_time.sens <- run_model_oil(oil_dat_time.sens, ~Length_cooking_time_in_s)
model_water_time.sens <- run_model_water(water_dat_time.sens, ~Length_cooking_time_in_s)
model_dry_time.sens <- run_model_dry(dry_dat_time.sens, ~Length_cooking_time_in_s)

summary(model_oil_time.sens)
## 
## Multivariate Meta-Analysis Model (k = 263; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -123.3924   246.7848   260.7848   285.7364   261.2275   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.2748  0.5242      5     no        Study_ID   no 
## sigma^2.2  0.0000  0.0001     15     no       Phylogeny  yes 
## sigma^2.3  0.0151  0.1230     15     no  Species_common   no 
## sigma^2.4  0.1430  0.3781     16     no       PFAS_type   no 
## sigma^2.5  0.0393  0.1982    263     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 261) = 750.9246, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 261) = 99.3161, p-val < .0001
## 
## Model Results:
## 
##                           estimate      se     tval   df    pval    ci.lb 
## intrcpt                     0.4430  0.2800   1.5817  261  0.1149  -0.1085 
## Length_cooking_time_in_s   -0.0015  0.0002  -9.9657  261  <.0001  -0.0018 
##                             ci.ub 
## intrcpt                    0.9944      
## Length_cooking_time_in_s  -0.0012  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_water_time.sens)
## 
## Multivariate Meta-Analysis Model (k = 120; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -100.6968   201.3935   215.3935   234.7883   216.4117   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.1337  0.3656      7     no        Study_ID   no 
## sigma^2.2  0.0076  0.0871     17     no       Phylogeny  yes 
## sigma^2.3  0.0000  0.0000     17     no  Species_common   no 
## sigma^2.4  0.0960  0.3098     15     no       PFAS_type   no 
## sigma^2.5  0.1786  0.4226    120     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 118) = 1103.1579, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 118) = 21.9447, p-val < .0001
## 
## Model Results:
## 
##                           estimate      se     tval   df    pval    ci.lb 
## intrcpt                     0.6521  0.2644   2.4663  118  0.0151   0.1285 
## Length_cooking_time_in_s   -0.0012  0.0002  -4.6845  118  <.0001  -0.0017 
##                             ci.ub 
## intrcpt                    1.1756    * 
## Length_cooking_time_in_s  -0.0007  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_dry_time.sens)
## 
## Multivariate Meta-Analysis Model (k = 47; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
##  -9.7496   19.4991   31.4991   42.3391   33.7096   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.0000  0.0000      1    yes        Study_ID   no 
## sigma^2.2  0.0038  0.0614      8     no       Phylogeny  yes 
## sigma^2.3  0.0125  0.1118      8     no  Species_common   no 
## sigma^2.4  0.0767  0.2769      2     no       PFAS_type   no 
## sigma^2.5  0.0000  0.0000     47     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 45) = 68.7069, p-val = 0.0130
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 45) = 64.9941, p-val < .0001
## 
## Model Results:
## 
##                           estimate      se     tval  df    pval    ci.lb 
## intrcpt                     0.4745  0.2610   1.8182  45  0.0757  -0.0511 
## Length_cooking_time_in_s   -0.0014  0.0002  -8.0619  45  <.0001  -0.0018 
##                             ci.ub 
## intrcpt                    1.0001    . 
## Length_cooking_time_in_s  -0.0011  *** 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_time.sens <- predict.rma(model_oil_time.sens)
pred_water_time.sens <- predict.rma(model_water_time.sens)
pred_dry_time.sens <- predict.rma(model_dry_time.sens)

oil_dat_time.sens <- mutate(oil_dat_time.sens, ci.lb = pred_oil_time.sens$ci.lb,
    ci.ub = pred_oil_time.sens$ci.ub, fit = pred_oil_time.sens$pred)

water_dat_time.sens <- mutate(water_dat_time.sens, ci.lb = pred_water_time.sens$ci.lb,
    ci.ub = pred_water_time.sens$ci.ub, fit = pred_water_time.sens$pred)

dry_dat_time.sens <- mutate(dry_dat_time.sens, ci.lb = pred_dry_time.sens$ci.lb,
    ci.ub = pred_dry_time.sens$ci.ub, fit = pred_dry_time.sens$pred)

For some reason the plot doesn’t want to knit, although the script works

# Actual plot

ggplot(dat.sens, aes(x = Length_cooking_time_in_s, y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = water_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.2) + geom_line(data = water_dat_time.sens, aes(y = fit), size = 1.5,
    col = "dodgerblue") +  col = "dodgerblue") +
geom_ribbon(data = oil_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.3) + geom_line(data = oil_dat_time.sens, aes(y = fit), size = 1.5,
    col = "goldenrod") +  col = "goldenrod") +
geom_ribbon(data = dry_dat_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.25) + geom_line(data = dry_dat_time.sens, aes(y = fit), size = 1.5,
    col = "palegreen3") +  col = "palegreen3") +

geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "Cooking time (s)",
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
        legend.direction = "horizontal", legend.title = element_text(size = 15),
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))
Predictions with the full model
##### Oil based
full_model_oil_time.sens<- run_model_oil(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Ratio_liquid_fish)))
summary(full_model_oil_time.sens)
## 
## Multivariate Meta-Analysis Model (k = 257; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -103.6149   207.2299   227.2299   262.5242   228.1427   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.1966  0.4434      5     no        Study_ID   no 
## sigma^2.2  0.0000  0.0000     15     no       Phylogeny  yes 
## sigma^2.3  0.0179  0.1336     15     no  Species_common   no 
## sigma^2.4  0.1114  0.3337     16     no       PFAS_type   no 
## sigma^2.5  0.0287  0.1694    257     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 252) = 547.0935, p-val < .0001
## 
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 27.8420, p-val < .0001
## 
## Model Results:
## 
##                                estimate      se      tval   df    pval    ci.lb 
## intrcpt                          0.4897  0.2498    1.9608  252  0.0510  -0.0021 
## scale(Temperature_in_Celsius)   -0.0679  0.1352   -0.5017  252  0.6163  -0.3342 
## Length_cooking_time_in_s        -0.0015  0.0001  -10.2481  252  <.0001  -0.0018 
## scale(PFAS_carbon_chain)         0.1421  0.0730    1.9471  252  0.0526  -0.0016 
## scale(log(Ratio_liquid_fish))   -0.1155  0.2578   -0.4481  252  0.6545  -0.6233 
##                                  ci.ub 
## intrcpt                         0.9816    . 
## scale(Temperature_in_Celsius)   0.1985      
## Length_cooking_time_in_s       -0.0012  *** 
## scale(PFAS_carbon_chain)        0.2858    . 
## scale(log(Ratio_liquid_fish))   0.3922      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_time.sens<-predict.rma(full_model_oil_time.sens, addx=TRUE, newmods=cbind(0,oil_dat.sens$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time.sens<-as.data.frame(pred_oil_time.sens)
pred_oil_time.sens$Length_cooking_time_in_s=pred_oil_time.sens$X.Length_cooking_time_in_s
pred_oil_time.sens<-left_join(oil_dat.sens, pred_oil_time.sens, by="Length_cooking_time_in_s")


##### Water based
full_model_water_time.sens<- run_model_water(water_dat.sens, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Ratio_liquid_fish)))

summary(full_model_water_time.sens)
## 
## Multivariate Meta-Analysis Model (k = 101; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -59.3606  118.7212  136.7212  159.8936  138.7901   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.1806  0.4250      5     no        Study_ID   no 
## sigma^2.2  0.0000  0.0000     13     no       Phylogeny  yes 
## sigma^2.3  0.0000  0.0000     13     no  Species_common   no 
## sigma^2.4  0.1187  0.3446     15     no       PFAS_type   no 
## sigma^2.5  0.0659  0.2567    101     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 97) = 330.7425, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 97) = 15.8996, p-val < .0001
## 
## Model Results:
## 
##                                estimate      se     tval  df    pval    ci.lb 
## intrcpt                          0.3223  0.2894   1.1134  97  0.2683  -0.2522 
## Length_cooking_time_in_s        -0.0013  0.0002  -6.1671  97  <.0001  -0.0018 
## scale(PFAS_carbon_chain)         0.1680  0.0819   2.0524  97  0.0428   0.0055 
## scale(log(Ratio_liquid_fish))   -0.3030  0.1596  -1.8984  97  0.0606  -0.6198 
##                                  ci.ub 
## intrcpt                         0.8967      
## Length_cooking_time_in_s       -0.0009  *** 
## scale(PFAS_carbon_chain)        0.3306    * 
## scale(log(Ratio_liquid_fish))   0.0138    . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_water_time.sens<-predict.rma(full_model_water_time.sens, addx=TRUE, newmods=cbind(water_dat.sens$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time.sens<-as.data.frame(pred_water_time.sens)
pred_water_time.sens$Length_cooking_time_in_s=pred_water_time.sens$X.Length_cooking_time_in_s
pred_water_time.sens<-left_join(water_dat, pred_water_time.sens, by="Length_cooking_time_in_s")

##### No liquid 

full_model_dry_time.sens<- run_model_dry(dry_dat.sens, ~ Length_cooking_time_in_s)

pred_dry_time.sens<-predict.rma(full_model_dry_time.sens, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time.sens<-as.data.frame(pred_dry_time.sens)
pred_dry_time.sens$Length_cooking_time_in_s=pred_dry_time.sens$X.Length_cooking_time_in_s
pred_dry_time.sens<-left_join(dry_dat.sens, pred_dry_time.sens, by="Length_cooking_time_in_s")




ggplot(dat.sens,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
  
       geom_ribbon(data=pred_water_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_time.sens,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
       geom_ribbon(data=pred_oil_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=pred_oil_time.sens,aes(y = pred), size = 1.5, col="goldenrod")+  
  
        geom_ribbon(data=pred_dry_time.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_time.sens,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Liquid volume to tissue sample ratio

Ratio_liquid_fish taken as NA for the dry cooking category
dat.sens.vol <- filter(dat.sens, Ratio_liquid_fish != "NA")
include <- row.names(cor_tree) %in% dat.sens.vol$Phylogeny  # Check which rows are present in the phylogenetic tree 
cor_tree_sens.vol <- cor_tree[include, include]  # Only include the species that match the reduced data set 
VCV_lnRR.sens.vol <- make_VCV_matrix(dat.sens.vol, V = "var_lnRR", cluster = "Cohort_ID",
    obs = "Effect_ID", rho = 0.5)  # create VCV matrix for the specified data


mod.sens.vol <- rma.mv(lnRR, VCV_lnRR.sens.vol, mods = ~log(Ratio_liquid_fish), random = list(~1 |
    Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
    R = list(Phylogeny = cor_tree_sens.vol), test = "t", data = dat.sens.vol)
summary(mod.sens.vol)
## 
## Multivariate Meta-Analysis Model (k = 398; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -367.3849   734.7698   748.7698   776.6397   749.0584   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.2778  0.5270      7     no        Study_ID   no 
## sigma^2.2  0.0434  0.2083     26     no       Phylogeny  yes 
## sigma^2.3  0.0929  0.3048     27     no  Species_common   no 
## sigma^2.4  0.1221  0.3494     18     no       PFAS_type   no 
## sigma^2.5  0.2322  0.4819    398     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 396) = 3756.6307, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 396) = 0.1389, p-val = 0.7096
## 
## Model Results:
## 
##                         estimate      se     tval   df    pval    ci.lb   ci.ub 
## intrcpt                  -0.1182  0.2678  -0.4415  396  0.6591  -0.6447  0.4083 
## log(Ratio_liquid_fish)   -0.0151  0.0404  -0.3726  396  0.7096  -0.0945  0.0644 
##  
## intrcpt 
## log(Ratio_liquid_fish) 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat.sens.vol, mod.sens.vol, log(dat.sens.vol$Ratio_liquid_fish),
    "ln(Liquid volume to tissue sample ratio (mL/g))") + scale_fill_manual(values = c("goldenrod2",
    "dodgerblue3"))

Ratio_liquid_fish taken as 0 for the dry cooking category
dat.sens.vol0 <- filter(dat.sens, Ratio_liquid_fish_0 != "NA")
include <- row.names(cor_tree) %in% dat.sens.vol0$Phylogeny  # Check which rows are present in the phylogenetic tree 
cor_tree_sens.vol0 <- cor_tree[include, include]  # Only include the species that match the reduced data set 
VCV_lnRR.sens.vol0 <- make_VCV_matrix(dat.sens.vol0, V = "var_lnRR", cluster = "Cohort_ID",
    obs = "Effect_ID", rho = 0.5)  # create VCV matrix for the specified data


mod.sens.vol0 <- rma.mv(lnRR, VCV_lnRR.sens.vol0, mods = ~log(Ratio_liquid_fish_0 +
    1), random = list(~1 | Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type,
    ~1 | Effect_ID), R = list(Phylogeny = cor_tree_sens.vol0), test = "t", data = dat.sens.vol0)
summary(mod.sens.vol0)
## 
## Multivariate Meta-Analysis Model (k = 467; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -424.5985   849.1970   863.1970   892.1913   863.4421   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.2773  0.5266      7     no        Study_ID   no 
## sigma^2.2  0.0431  0.2076     26     no       Phylogeny  yes 
## sigma^2.3  0.1304  0.3611     27     no  Species_common   no 
## sigma^2.4  0.1279  0.3576     18     no       PFAS_type   no 
## sigma^2.5  0.2269  0.4764    467     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 465) = 5227.9857, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 465) = 2.5539, p-val = 0.1107
## 
## Model Results:
## 
##                               estimate      se     tval   df    pval    ci.lb 
## intrcpt                        -0.0411  0.2740  -0.1500  465  0.8808  -0.5796 
## log(Ratio_liquid_fish_0 + 1)   -0.0448  0.0280  -1.5981  465  0.1107  -0.0999 
##                                ci.ub 
## intrcpt                       0.4974    
## log(Ratio_liquid_fish_0 + 1)  0.0103    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat.sens.vol0, mod.sens.vol0, log(dat.sens.vol0$Ratio_liquid_fish_0 +
    1), "ln(Liquid volume to tissue sample ratio + 1) (mL/g)") + scale_fill_manual(values = c("#55C667FF",
    "goldenrod2", "dodgerblue3"))

Effect of cooking time on lnRR for each cooking category
oil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")

oil_dat_vol.sens <- filter(oil_dat.sens, Ratio_liquid_fish != "NA")
water_dat_vol.sens <- filter(water_dat.sens, Ratio_liquid_fish != "NA")

model_oil_vol.sens <- run_model_oil(oil_dat_vol.sens, ~log(Ratio_liquid_fish))
model_water_vol.sens <- run_model_water(water_dat_vol.sens, ~log(Ratio_liquid_fish))

summary(model_oil_vol.sens)
## 
## Multivariate Meta-Analysis Model (k = 297; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -288.4232   576.8464   590.8464   616.6553   591.2367   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.4059  0.6371      6     no        Study_ID   no 
## sigma^2.2  0.0646  0.2541     23     no       Phylogeny  yes 
## sigma^2.3  0.0792  0.2815     24     no  Species_common   no 
## sigma^2.4  0.0837  0.2893     17     no       PFAS_type   no 
## sigma^2.5  0.2703  0.5199    297     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 295) = 3239.8130, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 295) = 0.0004, p-val = 0.9831
## 
## Model Results:
## 
##                         estimate      se     tval   df    pval    ci.lb   ci.ub 
## intrcpt                  -0.0787  0.3330  -0.2365  295  0.8132  -0.7341  0.5766 
## log(Ratio_liquid_fish)    0.0010  0.0465   0.0212  295  0.9831  -0.0905  0.0925 
##  
## intrcpt 
## log(Ratio_liquid_fish) 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_water_vol.sens)
## 
## Multivariate Meta-Analysis Model (k = 101; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -78.6093  157.2186  171.2186  189.3844  172.4494   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.5489  0.7409      5     no        Study_ID   no 
## sigma^2.2  0.0000  0.0000     13     no       Phylogeny  yes 
## sigma^2.3  0.0000  0.0000     13     no  Species_common   no 
## sigma^2.4  0.1371  0.3703     15     no       PFAS_type   no 
## sigma^2.5  0.1173  0.3424    101     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 99) = 501.2657, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 99) = 6.6516, p-val = 0.0114
## 
## Model Results:
## 
##                         estimate      se     tval  df    pval    ci.lb    ci.ub 
## intrcpt                   0.4829  0.4930   0.9796  99  0.3297  -0.4952   1.4611 
## log(Ratio_liquid_fish)   -0.4474  0.1735  -2.5791  99  0.0114  -0.7917  -0.1032 
##  
## intrcpt 
## log(Ratio_liquid_fish)  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_vol.sens <- predict.rma(model_oil_vol.sens)
pred_water_vol.sens <- predict.rma(model_water_vol.sens)

oil_dat_vol.sens <- mutate(oil_dat_vol.sens, ci.lb = pred_oil_vol.sens$ci.lb, ci.ub = pred_oil_vol.sens$ci.ub,
    fit = pred_oil_vol.sens$pred)

water_dat_vol.sens <- mutate(water_dat_vol.sens, ci.lb = pred_water_vol.sens$ci.lb,
    ci.ub = pred_water_vol.sens$ci.ub, fit = pred_water_vol.sens$pred)

For some reason the plot doesn’t want to knit, although the script works

ggplot(dat.sens, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = water_dat_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.2) + geom_line(data = water_dat_vol.sens, aes(y = fit), size = 1.5,
    col = "dodgerblue") +  col = "dodgerblue") +
geom_ribbon(data = oil_dat_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.3) + geom_line(data = oil_dat_vol.sens, aes(y = fit), size = 1.5, col = "goldenrod") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio)",
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
        legend.direction = "horizontal", legend.title = element_text(size = 15),
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))
Predictions with the full model
##### Oil based
full_model_oil_vol.sens <- run_model_oil(oil_dat.sens, ~scale(Temperature_in_Celsius) +
    scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)

summary(full_model_oil_vol.sens)
## 
## Multivariate Meta-Analysis Model (k = 257; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -103.6149   207.2299   227.2299   262.5242   228.1427   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.1966  0.4434      5     no        Study_ID   no 
## sigma^2.2  0.0000  0.0000     15     no       Phylogeny  yes 
## sigma^2.3  0.0179  0.1336     15     no  Species_common   no 
## sigma^2.4  0.1114  0.3337     16     no       PFAS_type   no 
## sigma^2.5  0.0287  0.1694    257     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 252) = 547.0935, p-val < .0001
## 
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 27.8420, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se      tval   df    pval 
## intrcpt                           -0.6350  0.2419   -2.6251  252  0.0092 
## scale(Temperature_in_Celsius)     -0.0679  0.1352   -0.5017  252  0.6163 
## scale(Length_cooking_time_in_s)   -0.3947  0.0385  -10.2481  252  <.0001 
## scale(PFAS_carbon_chain)           0.1421  0.0730    1.9471  252  0.0526 
## log_Ratio_liquid_fish             -0.0354  0.0790   -0.4481  252  0.6545 
##                                    ci.lb    ci.ub 
## intrcpt                          -1.1114  -0.1586   ** 
## scale(Temperature_in_Celsius)    -0.3342   0.1985      
## scale(Length_cooking_time_in_s)  -0.4706  -0.3189  *** 
## scale(PFAS_carbon_chain)         -0.0016   0.2858    . 
## log_Ratio_liquid_fish            -0.1909   0.1201      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_vol.sens <- predict.rma(full_model_oil_vol.sens, addx = TRUE, newmods = cbind(0,
    0, 0, oil_dat.sens$log_Ratio_liquid_fish))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_oil_vol.sens <- as.data.frame(pred_oil_vol.sens)
pred_oil_vol.sens <- pred_oil_vol.sens %>%
    mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "oil-based",
        lnRR = 0)  # for the plot to work, we need to add a column with cooking category and a column with lnRR


##### Water based

full_model_water_vol.sens <- run_model_water(water_dat.sens, ~scale(Temperature_in_Celsius) +
    scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)

summary(full_model_water_vol.sens)
## 
## Multivariate Meta-Analysis Model (k = 101; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -59.3606  118.7212  136.7212  159.8936  138.7901   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.1806  0.4250      5     no        Study_ID   no 
## sigma^2.2  0.0000  0.0000     13     no       Phylogeny  yes 
## sigma^2.3  0.0000  0.0000     13     no  Species_common   no 
## sigma^2.4  0.1187  0.3446     15     no       PFAS_type   no 
## sigma^2.5  0.0659  0.2567    101     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 97) = 330.7425, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 97) = 15.8996, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     tval  df    pval    ci.lb 
## intrcpt                            0.0415  0.3623   0.1145  97  0.9091  -0.6777 
## scale(Length_cooking_time_in_s)   -0.4700  0.0762  -6.1671  97  <.0001  -0.6213 
## scale(PFAS_carbon_chain)           0.1680  0.0819   2.0524  97  0.0428   0.0055 
## log_Ratio_liquid_fish             -0.2639  0.1390  -1.8984  97  0.0606  -0.5397 
##                                    ci.ub 
## intrcpt                           0.7607      
## scale(Length_cooking_time_in_s)  -0.3187  *** 
## scale(PFAS_carbon_chain)          0.3306    * 
## log_Ratio_liquid_fish             0.0120    . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_water_vol.sens <- predict.rma(full_model_water_vol.sens, addx = TRUE, newmods = cbind(0,
    0, water_dat.sens$log_Ratio_liquid_fish))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_water_vol.sens <- as.data.frame(pred_water_vol.sens)
pred_water_vol.sens <- pred_water_vol.sens %>%
    mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "water-based",
        lnRR = 0)

For some reason the plot doesn’t want to knit, although the script works

ggplot(dat.sens, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = pred_water_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.2) + geom_line(data = pred_water_vol.sens, aes(y = pred), size = 1.5,
    col = "dodgerblue") +  col = "dodgerblue") +
geom_ribbon(data = pred_oil_vol.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.3) + geom_line(data = pred_oil_vol.sens, aes(y = pred), size = 1.5,
    col = "goldenrod") +  col = "goldenrod") +

geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio)",
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
        legend.direction = "horizontal", legend.title = element_text(size = 15),
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))  #### The line doesn't go all the way down (the predict function doesn't capture the biggest values)

PFAS carbon chain

dat.sens.PFAS <- filter(dat.sens, PFAS_carbon_chain != "NA")
include <- row.names(cor_tree) %in% dat.sens.PFAS$Phylogeny  # Check which rows are present in the phylogenetic tree 
cor_tree_sens.PFAS <- cor_tree[include, include]  # Only include the species that match the reduced data set 
VCV_lnRR.sens.PFAS <- make_VCV_matrix(dat.sens.PFAS, V = "var_lnRR", cluster = "Cohort_ID",
    obs = "Effect_ID", rho = 0.5)  # create VCV matrix for the specified data


mod.sens.PFAS <- rma.mv(lnRR, VCV_lnRR.sens.PFAS, mods = ~PFAS_carbon_chain, random = list(~1 |
    Study_ID, ~1 | Phylogeny, ~1 | Species_common, ~1 | PFAS_type, ~1 | Effect_ID),
    R = list(Phylogeny = cor_tree_sens.PFAS), test = "t", data = dat.sens.PFAS)
summary(mod.sens.PFAS)
## 
## Multivariate Meta-Analysis Model (k = 486; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -451.3336   902.6673   916.6673   945.9419   916.9026   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.2362  0.4860      9     no        Study_ID   no 
## sigma^2.2  0.0932  0.3054     30     no       Phylogeny  yes 
## sigma^2.3  0.1079  0.3284     31     no  Species_common   no 
## sigma^2.4  0.0902  0.3003     18     no       PFAS_type   no 
## sigma^2.5  0.2440  0.4939    486     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 484) = 6303.2923, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 484) = 1.3174, p-val = 0.2516
## 
## Model Results:
## 
##                    estimate      se     tval   df    pval    ci.lb   ci.ub 
## intrcpt             -0.2521  0.3602  -0.6999  484  0.4844  -0.9599  0.4557    
## PFAS_carbon_chain    0.0307  0.0268   1.1478  484  0.2516  -0.0219  0.0834    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
plot_continuous(dat.sens.PFAS, mod.sens.PFAS, dat.sens.PFAS$PFAS_carbon_chain, "PFAS carbon chain length")  # The relationship with cooking time appears even stronger

Effect of carbon chain length on lnRR for each cooking category
oil_dat.sens <- filter(dat.sens, Cooking_Category == "oil-based")
water_dat.sens <- filter(dat.sens, Cooking_Category == "water-based")
dry_dat.sens <- filter(dat.sens, Cooking_Category == "No liquid")


oil_dat_PFAS.sens <- filter(oil_dat.sens, PFAS_carbon_chain != "NA")
water_dat_PFAS.sens <- filter(water_dat.sens, PFAS_carbon_chain != "NA")
dry_dat_PFAS.sens <- filter(dry_dat.sens, PFAS_carbon_chain != "NA")

model_oil_PFAS.sens <- run_model_oil(oil_dat_PFAS.sens, ~PFAS_carbon_chain)
model_water_PFAS.sens <- run_model_water(water_dat_PFAS.sens, ~PFAS_carbon_chain)
model_dry_PFAS.sens <- run_model_dry(dry_dat_PFAS.sens, ~PFAS_carbon_chain)

summary(model_oil_PFAS.sens)
## 
## Multivariate Meta-Analysis Model (k = 297; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -288.4769   576.9538   590.9538   616.7626   591.3440   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.3725  0.6103      6     no        Study_ID   no 
## sigma^2.2  0.0727  0.2697     23     no       Phylogeny  yes 
## sigma^2.3  0.0773  0.2780     24     no  Species_common   no 
## sigma^2.4  0.0747  0.2733     17     no       PFAS_type   no 
## sigma^2.5  0.2709  0.5205    297     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 295) = 3551.6546, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 295) = 1.4816, p-val = 0.2245
## 
## Model Results:
## 
##                    estimate      se     tval   df    pval    ci.lb   ci.ub 
## intrcpt             -0.3791  0.4123  -0.9196  295  0.3585  -1.1905  0.4322    
## PFAS_carbon_chain    0.0344  0.0283   1.2172  295  0.2245  -0.0212  0.0900    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_water_PFAS.sens)
## 
## Multivariate Meta-Analysis Model (k = 120; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -109.5717   219.1435   233.1435   252.5383   234.1617   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.1362  0.3691      7     no        Study_ID   no 
## sigma^2.2  0.0699  0.2643     17     no       Phylogeny  yes 
## sigma^2.3  0.0000  0.0000     17     no  Species_common   no 
## sigma^2.4  0.0706  0.2657     15     no       PFAS_type   no 
## sigma^2.5  0.2176  0.4665    120     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 118) = 1199.5376, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 118) = 2.0593, p-val = 0.1539
## 
## Model Results:
## 
##                    estimate      se     tval   df    pval    ci.lb   ci.ub 
## intrcpt             -0.5743  0.3686  -1.5583  118  0.1218  -1.3042  0.1555    
## PFAS_carbon_chain    0.0480  0.0334   1.4350  118  0.1539  -0.0182  0.1141    
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
summary(model_dry_PFAS.sens)
## 
## Multivariate Meta-Analysis Model (k = 69; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -67.7369  135.4737  149.4737  164.9066  151.3721   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.4774  0.6909      2     no        Study_ID   no 
## sigma^2.2  0.1980  0.4450     13     no       Phylogeny  yes 
## sigma^2.3  0.0483  0.2198     14     no  Species_common   no 
## sigma^2.4  0.0170  0.1305      7     no       PFAS_type   no 
## sigma^2.5  0.2878  0.5364     69     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 67) = 1192.4310, p-val < .0001
## 
## Test of Moderators (coefficient 2):
## F(df1 = 1, df2 = 67) = 5.2409, p-val = 0.0252
## 
## Model Results:
## 
##                    estimate      se     tval  df    pval    ci.lb   ci.ub 
## intrcpt             -1.1648  0.8497  -1.3708  67  0.1750  -2.8607  0.5312    
## PFAS_carbon_chain    0.1611  0.0704   2.2893  67  0.0252   0.0206  0.3016  * 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_PFAS.sens <- predict.rma(model_oil_PFAS.sens)
pred_water_PFAS.sens <- predict.rma(model_water_PFAS.sens)
pred_dry_PFAS.sens <- predict.rma(model_dry_PFAS.sens)

oil_dat_PFAS.sens <- mutate(oil_dat_PFAS.sens, ci.lb = pred_oil_PFAS.sens$ci.lb,
    ci.ub = pred_oil_PFAS.sens$ci.ub, fit = pred_oil_PFAS.sens$pred)

water_dat_PFAS.sens <- mutate(water_dat_PFAS.sens, ci.lb = pred_water_PFAS.sens$ci.lb,
    ci.ub = pred_water_PFAS.sens$ci.ub, fit = pred_water_PFAS.sens$pred)

dry_dat_PFAS.sens <- mutate(dry_dat_PFAS.sens, ci.lb = pred_dry_PFAS.sens$ci.lb,
    ci.ub = pred_dry_PFAS.sens$ci.ub, fit = pred_dry_PFAS.sens$pred)

For some reason the plot doesn’t want to knit, although the script works

ggplot(dat.sens, aes(x = PFAS_carbon_chain, y = lnRR, fill = Cooking_Category)) +
geom_ribbon(data = dry_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.2) + geom_line(data = dry_dat_PFAS.sens, aes(y = fit), size = 1.5,
    col = "palegreen3") +  col = "palegreen3") +
geom_ribbon(data = oil_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.3) + geom_line(data = oil_dat_PFAS.sens, aes(y = fit), size = 1.5,
    col = "goldenrod") +  col = "goldenrod") +
geom_ribbon(data = water_dat_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.3) + geom_line(data = water_dat_PFAS.sens, aes(y = fit), size = 1.5,
    col = "dodgerblue") +  col = "dodgerblue") +
geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "PFAS carbon chain length",
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
        legend.position = c(0, 0), legend.justification = c(0, 0), legend.background = element_blank(),
        legend.direction = "horizontal", legend.title = element_text(size = 15),
        panel.border = element_rect(colour = "black", fill = NA, size = 1.2))
Predictions with the full model
##### Oil based
full_model_oil_PFAS.sens<- run_model_oil(oil_dat.sens, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Ratio_liquid_fish)))
summary(full_model_oil_PFAS.sens)
## 
## Multivariate Meta-Analysis Model (k = 257; method: REML)
## 
##    logLik   Deviance        AIC        BIC       AICc 
## -103.6149   207.2299   227.2299   262.5242   228.1427   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.1966  0.4434      5     no        Study_ID   no 
## sigma^2.2  0.0000  0.0000     15     no       Phylogeny  yes 
## sigma^2.3  0.0179  0.1336     15     no  Species_common   no 
## sigma^2.4  0.1114  0.3337     16     no       PFAS_type   no 
## sigma^2.5  0.0287  0.1694    257     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 252) = 547.0935, p-val < .0001
## 
## Test of Moderators (coefficients 2:5):
## F(df1 = 4, df2 = 252) = 27.8420, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se      tval   df    pval 
## intrcpt                           -1.1519  0.3612   -3.1889  252  0.0016 
## scale(Temperature_in_Celsius)     -0.0679  0.1352   -0.5017  252  0.6163 
## scale(Length_cooking_time_in_s)   -0.3947  0.0385  -10.2481  252  <.0001 
## PFAS_carbon_chain                  0.0575  0.0296    1.9471  252  0.0526 
## scale(log(Ratio_liquid_fish))     -0.1155  0.2578   -0.4481  252  0.6545 
##                                    ci.lb    ci.ub 
## intrcpt                          -1.8633  -0.4405   ** 
## scale(Temperature_in_Celsius)    -0.3342   0.1985      
## scale(Length_cooking_time_in_s)  -0.4706  -0.3189  *** 
## PFAS_carbon_chain                -0.0007   0.1157    . 
## scale(log(Ratio_liquid_fish))    -0.6233   0.3922      
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_oil_PFAS.sens<-predict.rma(full_model_oil_PFAS.sens, addx=TRUE, newmods=cbind(0,0, oil_dat.sens$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS.sens<-as.data.frame(pred_oil_PFAS.sens)
pred_oil_PFAS.sens$PFAS_carbon_chain=pred_oil_PFAS.sens$X.PFAS_carbon_chain
pred_oil_PFAS.sens<-left_join(oil_dat.sens, pred_oil_PFAS.sens, by="PFAS_carbon_chain")


##### Water based
full_model_water_PFAS.sens<- run_model_water(water_dat.sens, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Ratio_liquid_fish)))

summary(full_model_water_PFAS.sens)
## 
## Multivariate Meta-Analysis Model (k = 101; method: REML)
## 
##   logLik  Deviance       AIC       BIC      AICc 
## -59.3606  118.7212  136.7212  159.8936  138.7901   
## 
## Variance Components:
## 
##             estim    sqrt  nlvls  fixed          factor    R 
## sigma^2.1  0.1806  0.4250      5     no        Study_ID   no 
## sigma^2.2  0.0000  0.0000     13     no       Phylogeny  yes 
## sigma^2.3  0.0000  0.0000     13     no  Species_common   no 
## sigma^2.4  0.1187  0.3446     15     no       PFAS_type   no 
## sigma^2.5  0.0659  0.2567    101     no       Effect_ID   no 
## 
## Test for Residual Heterogeneity:
## QE(df = 97) = 330.7425, p-val < .0001
## 
## Test of Moderators (coefficients 2:4):
## F(df1 = 3, df2 = 97) = 15.8996, p-val < .0001
## 
## Model Results:
## 
##                                  estimate      se     tval  df    pval    ci.lb 
## intrcpt                           -1.2501  0.3896  -3.2083  97  0.0018  -2.0234 
## scale(Length_cooking_time_in_s)   -0.4700  0.0762  -6.1671  97  <.0001  -0.6213 
## PFAS_carbon_chain                  0.0742  0.0361   2.0524  97  0.0428   0.0024 
## scale(log(Ratio_liquid_fish))     -0.3030  0.1596  -1.8984  97  0.0606  -0.6198 
##                                    ci.ub 
## intrcpt                          -0.4768   ** 
## scale(Length_cooking_time_in_s)  -0.3187  *** 
## PFAS_carbon_chain                 0.1459    * 
## scale(log(Ratio_liquid_fish))     0.0138    . 
## 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
pred_water_PFAS.sens<-predict.rma(full_model_water_PFAS.sens, addx=TRUE, newmods=cbind(0, water_dat.sens$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS.sens<-as.data.frame(pred_water_PFAS.sens)
pred_water_PFAS.sens$PFAS_carbon_chain=pred_water_PFAS.sens$X.PFAS_carbon_chain
pred_water_PFAS.sens<-left_join(water_dat.sens, pred_water_PFAS.sens, by="PFAS_carbon_chain")

##### No liquid 

full_model_dry_PFAS.sens<- run_model_dry(dry_dat.sens, ~ PFAS_carbon_chain)

pred_dry_PFAS.sens<-predict.rma(full_model_dry_PFAS.sens, addx=TRUE)
pred_dry_PFAS.sens<-as.data.frame(pred_dry_PFAS.sens)
pred_dry_PFAS.sens$PFAS_carbon_chain=pred_dry_PFAS.sens$X.PFAS_carbon_chain
pred_dry_PFAS.sens<-left_join(dry_dat.sens, pred_dry_PFAS.sens, by="PFAS_carbon_chain")



ggplot(dat.sens,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
  
    
       geom_ribbon(data=pred_dry_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_PFAS.sens,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_ribbon(data=pred_water_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_PFAS.sens,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
  
       geom_ribbon(data=pred_oil_PFAS.sens, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=pred_oil_PFAS.sens,aes(y = pred), size = 1.5, col="goldenrod")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(0,0), 
          legend.justification = c(0,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Funnel plot

full_mod.sens <- run_model(dat.sens, ~-1 + Cooking_Category + scale(Temperature_in_Celsius) +
    scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + scale(log(Ratio_liquid_fish)))


funnel(full_mod.sens, yaxis = "seinv")

Figures for publication

Figure 2

Cooking time

full_model_time<- run_model(dat, ~     scale(Temperature_in_Celsius) +
                                       Length_cooking_time_in_s+
                                       scale(PFAS_carbon_chain) +
                                       scale(log(Ratio_liquid_fish)))

pred_full_model_time<-predict.rma(full_model_time, addx=TRUE, newmods=cbind(0,dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_time<-as.data.frame(pred_full_model_time)
pred_full_model_time$Length_cooking_time_in_s=pred_full_model_time$X.Length_cooking_time_in_s
pred_full_model_time<-left_join(dat, pred_full_model_time, by="Length_cooking_time_in_s")



uni_model_time<- run_model(dat, ~ Length_cooking_time_in_s)

pred_uni_model_time<-predict.rma(uni_model_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_time<-as.data.frame(pred_uni_model_time)
pred_uni_model_time$Length_cooking_time_in_s=pred_uni_model_time$X.Length_cooking_time_in_s
pred_uni_model_time<-left_join(dat, pred_uni_model_time, by="Length_cooking_time_in_s")



p_time<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_full_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_full_model_time,aes(y = pred), size = 1.5, color="orangered2")+  
  
       geom_ribbon(data=pred_uni_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
       geom_line(data=pred_uni_model_time,aes(y = pred), size = 1.5, col="gray30")+  
  
       labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Liquid volume to tissue sample ratio

full_model_vol<- run_model(dat, ~      scale(Temperature_in_Celsius) +
                                       scale(Length_cooking_time_in_s)+
                                       scale(PFAS_carbon_chain) +
                                       log_Ratio_liquid_fish)

pred_full_model_vol<-predict.rma(full_model_vol, addx=TRUE, newmods=cbind(0,0, 0, dat$log_Ratio_liquid_fish))
pred_full_model_vol<-as.data.frame(pred_full_model_vol)
pred_full_model_vol$log_Ratio_liquid_fish=pred_full_model_vol$X.log_Ratio_liquid_fish
pred_full_model_vol<- pred_full_model_vol %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), lnRR = 0) 



uni_model_vol<- run_model(dat, ~ log_Ratio_liquid_fish)

pred_uni_model_vol<-predict.rma(uni_model_vol, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_vol<-as.data.frame(pred_uni_model_vol)
pred_uni_model_vol$log_Ratio_liquid_fish=pred_uni_model_vol$X.log_Ratio_liquid_fish
pred_uni_model_vol<- pred_uni_model_vol %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), lnRR = 0) 



p_vol<-ggplot(dat,aes(x = log_Ratio_liquid_fish, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_full_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_full_model_vol,aes(y = pred), size = 1.5, color="orangered2")+  
  
       geom_ribbon(data=pred_uni_model_vol, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
       geom_line(data=pred_uni_model_vol,aes(y = pred), size = 1.5, col="gray30")+  
  
       labs(x = "ln (Liquid volume to tissue sample ratio) (mL/g)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.position="none", 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Cooking temperature

full_model_temp<- run_model(dat, ~     Temperature_in_Celsius +
                                       scale(Length_cooking_time_in_s)+
                                       scale(PFAS_carbon_chain) +
                                       scale(log(Ratio_liquid_fish)))

pred_full_model_temp<-predict.rma(full_model_temp, addx=TRUE, newmods=cbind(dat$Temperature_in_Celsius,0, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_temp<-as.data.frame(pred_full_model_temp)
pred_full_model_temp$Temperature_in_Celsius=pred_full_model_temp$X.Temperature_in_Celsius
pred_full_model_temp<-left_join(dat, pred_full_model_temp, by="Temperature_in_Celsius")



uni_model_temp<- run_model(dat, ~ Temperature_in_Celsius)

pred_uni_model_temp<-predict.rma(uni_model_temp, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_temp<-as.data.frame(pred_uni_model_temp)
pred_uni_model_temp$Temperature_in_Celsius=pred_uni_model_temp$X.Temperature_in_Celsius
pred_uni_model_temp<-left_join(dat, pred_uni_model_temp, by="Temperature_in_Celsius")



p_temp<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_full_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_full_model_temp,aes(y = pred), size = 1.5, color="orangered2")+  
  
       geom_ribbon(data=pred_uni_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
       geom_line(data=pred_uni_model_temp,aes(y = pred), size = 1.5, col="gray30")+  
  
       labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(1,0), 
          legend.justification = c(1,0),
          legend.background = element_blank(), 
          legend.direction="vertical",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

PFAS carbon chain length

full_model_PFAS<- run_model(dat, ~     scale(Temperature_in_Celsius) +
                                       scale(Length_cooking_time_in_s)+
                                       PFAS_carbon_chain +
                                       scale(log(Ratio_liquid_fish)))

pred_full_model_PFAS<-predict.rma(full_model_PFAS, addx=TRUE, newmods=cbind(0, 0, dat$PFAS_carbon_chain, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_PFAS<-as.data.frame(pred_full_model_PFAS)
pred_full_model_PFAS$PFAS_carbon_chain=pred_full_model_PFAS$X.PFAS_carbon_chain
pred_full_model_PFAS<-left_join(dat, pred_full_model_PFAS, by="PFAS_carbon_chain")



uni_model_PFAS<- run_model(dat, ~ PFAS_carbon_chain)

pred_uni_model_PFAS<-predict.rma(uni_model_PFAS, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_PFAS<-as.data.frame(pred_uni_model_PFAS)
pred_uni_model_PFAS$PFAS_carbon_chain=pred_uni_model_PFAS$X.PFAS_carbon_chain
pred_uni_model_PFAS<-left_join(dat, pred_uni_model_PFAS, by="PFAS_carbon_chain")



p_PFAS<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_full_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_full_model_PFAS,aes(y = pred), size = 1.5, color="orangered2")+  
  
       geom_ribbon(data=pred_uni_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
       geom_line(data=pred_uni_model_PFAS,aes(y = pred), size = 1.5, col="gray30")+  
  
       labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Combine plots and save

(p_time + p_vol)/(p_temp + p_PFAS) + plot_annotation(tag_levels = c("A", "B", "C",
    "D"))

ggsave("fig/Fig_2.png", width = 15, height = 12, dpi = 1200)

Figure 2, with Ratio_liquid_fish taken as 0 for the dry cooking method

Cooking time

full_model_time0<- run_model(dat, ~     scale(Temperature_in_Celsius) +
                                       Length_cooking_time_in_s+
                                       scale(PFAS_carbon_chain) +
                                       scale(log(Ratio_liquid_fish_0 + 1)))

pred_full_model_time0<-predict.rma(full_model_time0, addx=TRUE, newmods=cbind(0,dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_time0<-as.data.frame(pred_full_model_time0)
pred_full_model_time0$Length_cooking_time_in_s=pred_full_model_time0$X.Length_cooking_time_in_s
pred_full_model_time0<-left_join(dat, pred_full_model_time0, by="Length_cooking_time_in_s")



uni_model_time<- run_model(dat, ~ Length_cooking_time_in_s)

pred_uni_model_time<-predict.rma(uni_model_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_time<-as.data.frame(pred_uni_model_time)
pred_uni_model_time$Length_cooking_time_in_s=pred_uni_model_time$X.Length_cooking_time_in_s
pred_uni_model_time<-left_join(dat, pred_uni_model_time, by="Length_cooking_time_in_s")



p_time0<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_full_model_time0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_full_model_time0,aes(y = pred), size = 1.5, color="orangered2")+  
  
       geom_ribbon(data=pred_uni_model_time, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
       geom_line(data=pred_uni_model_time,aes(y = pred), size = 1.5, col="gray30")+  
  
       labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Liquid volume to tissue sample ratio

full_model_vol0<- run_model(dat, ~     scale(Temperature_in_Celsius) +
                                       scale(Length_cooking_time_in_s)+
                                       scale(PFAS_carbon_chain) +
                                       log_Ratio_liquid_fish0)

pred_full_model_vol0<-predict.rma(full_model_vol0, addx=TRUE, newmods=cbind(0,0, 0, dat$log_Ratio_liquid_fish0))
pred_full_model_vol0<-as.data.frame(pred_full_model_vol0)
pred_full_model_vol0$log_Ratio_liquid_fish0=pred_full_model_vol0$X.log_Ratio_liquid_fish
pred_full_model_vol0<- pred_full_model_vol0 %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish0)-1, lnRR = 0) 



uni_model_vol0<- run_model(dat, ~ log_Ratio_liquid_fish0)

pred_uni_model_vol0<-predict.rma(uni_model_vol0, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_vol0<-as.data.frame(pred_uni_model_vol0)
pred_uni_model_vol0$log_Ratio_liquid_fish0=pred_uni_model_vol0$X.log_Ratio_liquid_fish
pred_uni_model_vol0<- pred_uni_model_vol0 %>% mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish0) -1, lnRR = 0) 



p_vol0<-ggplot(dat,aes(x = log_Ratio_liquid_fish0, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_full_model_vol0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_full_model_vol0,aes(y = pred), size = 1.5, color="orangered2")+  
  
       geom_ribbon(data=pred_uni_model_vol0, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
       geom_line(data=pred_uni_model_vol0,aes(y = pred), size = 1.5, col="gray30")+  
  
       labs(x = "ln (Liquid volume to tissue sample ratio + 1) (mL/g)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.position="none", 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Cooking temperature

full_model_temp0<- run_model(dat, ~     Temperature_in_Celsius +
                                       scale(Length_cooking_time_in_s)+
                                       scale(PFAS_carbon_chain) +
                                       scale(log(Ratio_liquid_fish_0 + 1)))

pred_full_model_temp0<-predict.rma(full_model_temp0, addx=TRUE, newmods=cbind(dat$Temperature_in_Celsius,0, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_temp0<-as.data.frame(pred_full_model_temp0)
pred_full_model_temp0$Temperature_in_Celsius=pred_full_model_temp0$X.Temperature_in_Celsius
pred_full_model_temp0<-left_join(dat, pred_full_model_temp0, by="Temperature_in_Celsius")



uni_model_temp<- run_model(dat, ~ Temperature_in_Celsius)

pred_uni_model_temp<-predict.rma(uni_model_temp, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_temp<-as.data.frame(pred_uni_model_temp)
pred_uni_model_temp$Temperature_in_Celsius=pred_uni_model_temp$X.Temperature_in_Celsius
pred_uni_model_temp<-left_join(dat, pred_uni_model_temp, by="Temperature_in_Celsius")



p_temp0<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_full_model_temp0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_full_model_temp0,aes(y = pred), size = 1.5, color="orangered2")+  
  
       geom_ribbon(data=pred_uni_model_temp, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
       geom_line(data=pred_uni_model_temp,aes(y = pred), size = 1.5, col="gray30")+  
  
       labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(1,0), 
          legend.justification = c(1,0),
          legend.background = element_blank(), 
          legend.direction="vertical",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

PFAS carbon chain length

full_model_PFAS0<- run_model(dat, ~     scale(Temperature_in_Celsius) +
                                       scale(Length_cooking_time_in_s)+
                                       PFAS_carbon_chain +
                                       scale(log(Ratio_liquid_fish_0 + 1)))

pred_full_model_PFAS0<-predict.rma(full_model_PFAS0, addx=TRUE, newmods=cbind(0, 0, dat$PFAS_carbon_chain, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_full_model_PFAS0<-as.data.frame(pred_full_model_PFAS0)
pred_full_model_PFAS0$PFAS_carbon_chain=pred_full_model_PFAS0$X.PFAS_carbon_chain
pred_full_model_PFAS0<-left_join(dat, pred_full_model_PFAS0, by="PFAS_carbon_chain")



uni_model_PFAS<- run_model(dat, ~ PFAS_carbon_chain)

pred_uni_model_PFAS<-predict.rma(uni_model_PFAS, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_uni_model_PFAS<-as.data.frame(pred_uni_model_PFAS)
pred_uni_model_PFAS$PFAS_carbon_chain=pred_uni_model_PFAS$X.PFAS_carbon_chain
pred_uni_model_PFAS<-left_join(dat, pred_uni_model_PFAS, by="PFAS_carbon_chain")



p_PFAS0<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_full_model_PFAS0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_full_model_PFAS0,aes(y = pred), size = 1.5, color="orangered2")+  
  
       geom_ribbon(data=pred_uni_model_PFAS, aes(ymin = ci.lb, ymax = ci.ub), alpha=0.25, fill="gray40") +
       geom_line(data=pred_uni_model_PFAS,aes(y = pred), size = 1.5, col="gray30")+  
  
       labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Combine plots and save

(p_time0 + p_vol0)/(p_temp0 + p_PFAS0) + plot_annotation(tag_levels = c("A", "B",
    "C", "D"))

ggsave("fig/Fig_2_zero_ratio.png", width = 15, height = 12, dpi = 1200)

Figure 3

Adapt orchard_plot function

my_orchard<-function (object, mod = "Int", xlab, N = "none", 
    alpha = 0.5, angle = 90, cb = FALSE, transfm = c("none", 
        "tanh"), condition.lab = "Condition") 
{
    transfm <- match.arg(transfm)
    if (any(class(object) %in% c("rma.mv", "rma"))) {
        if (mod != "Int") {
            object <- mod_results(object, mod)
        }
        else {
            object <- mod_results(object, mod = "Int")
        }
    }
    mod_table <- object$mod_table
    data <- object$data
    data$moderator <- factor(data$moderator, levels = mod_table$name, 
        labels = mod_table$name)
    data$scale <- (1/sqrt(data[, "vi"]))
    legend <- "Precision (1/SE)"
    if (any(N != "none")) {
        data$scale <- N
        legend <- "Sample Size (N)"
    }
    if (transfm == "tanh") {
        cols <- sapply(mod_table, is.numeric)
        mod_table[, cols] <- Zr_to_r(mod_table[, cols])
        data$yi <- Zr_to_r(data$yi)
        label <- xlab
    }
    else {
        label <- xlab
    }
    mod_table$K <- as.vector(by(data, data[, "moderator"], 
        function(x) length(x[, "yi"])))
    group_no <- length(unique(mod_table[, "name"]))
    cbpl <- c("#55C667FF", "goldenrod2", "dodgerblue3") # change colors
    if (names(mod_table)[2] == "condition") {
        condition_no <- length(unique(mod_table[, "condition"]))
        plot <- ggplot2::ggplot() + ggbeeswarm::geom_quasirandom(data = data, 
            ggplot2::aes(y = yi, x = moderator, size = scale, 
                color = moderator), alpha = alpha) + ggplot2::geom_hline(yintercept = 0, 
            linetype = 2, colour = "black", alpha = alpha) + 
            ggplot2::geom_linerange(data = mod_table, ggplot2::aes(x = name, 
                ymin = lowerPR, ymax = upperPR), size = 0.75, # change size confidence intervals and swap CL with PR. Added whiskers
                position = ggplot2::position_dodge2(width = 0.3)) + 
            ggplot2::geom_pointrange(data = mod_table, ggplot2::aes(y = estimate, 
                x = name, ymin = lowerCL, ymax = upperCL, shape = as.factor(condition), # swap CL with PR
                fill = name), size = 1.6, stroke=2.2, width= 1.3, position = ggplot2::position_dodge2(width = 0.3)) + # change size point and prediction intervals
            ggplot2::scale_shape_manual(values = 20 + (1:condition_no)) + 
            ggplot2::coord_flip() + ggplot2::theme_bw() + ggplot2::guides(fill = "none", 
            colour = "none") + ggplot2::theme(legend.position = c(0, 
            1), legend.justification = c(0, 1)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) + 
            ggplot2::theme(legend.direction = "horizontal") + 
            ggplot2::theme(legend.background = ggplot2::element_blank()) + 
            ggplot2::labs(y = label, x = "", size = legend) + 
            ggplot2::labs(shape = condition.lab) + ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10, 
            colour = "black", hjust = 0.5, angle = angle))
    }
    else {
        plot <- ggplot2::ggplot(data = mod_table, ggplot2::aes(x = estimate, 
            y = name)) + ggbeeswarm::geom_quasirandom(data = data, 
            ggplot2::aes(x = yi, y = moderator, size = scale, 
                colour = moderator), groupOnX = FALSE, alpha = alpha) + 
            ggplot2::geom_errorbarh(ggplot2::aes(xmin = lowerPR, 
                xmax = upperPR), height = 0, show.legend = FALSE, # change error barrs
                size = 0.75, alpha = 0.5) + ggplot2::geom_errorbarh(ggplot2::aes(xmin = lowerCL, 
            xmax = upperCL), height = 0.1, show.legend = FALSE, 
            size = 1.75) + ggplot2::geom_vline(xintercept = 0, 
            linetype = 2, colour = "black", alpha = alpha) + 
            ggplot2::geom_point(ggplot2::aes(fill = name), size = 8,  # change point size
                shape = 21) + ggplot2::theme_bw() + ggplot2::guides(fill = "none", 
            colour = "none") + ggplot2::theme(legend.position = c(1, 
            0), legend.justification = c(1, 0)) + ggplot2::theme(legend.title = ggplot2::element_text(size = 9)) + 
            ggplot2::theme(legend.direction = "horizontal") + 
            ggplot2::theme(legend.background = ggplot2::element_blank()) + 
            ggplot2::labs(x = label, y = "", size = legend) + 
            ggplot2::theme(axis.text.y = ggplot2::element_text(size = 10, 
                colour = "black", hjust = 0.5, angle = angle))

    }
    if (cb == TRUE) {
        plot <- plot + ggplot2::scale_fill_manual(values = cbpl) + 
            ggplot2::scale_colour_manual(values = cbpl)
    }
    return(plot)
}

Run full models in original units

full_model_org_units <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
    Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)

# full model with Ratio_liquid_fish taken as `0` for the dry cooking category
full_model_org_units0 <- run_model(dat, ~-1 + Cooking_Category + Temperature_in_Celsius +
    Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish0)

# full model without the 'No liquid' data for figure 3B, when Ratio_liquid_fish
# is taken as `NA` for the dry cooking category
full_model_org_units_oil_water <- run_model(dat_oil_water, ~-1 + Cooking_Category +
    Temperature_in_Celsius + Length_cooking_time_in_s + PFAS_carbon_chain + log_Ratio_liquid_fish)

Models with Ratio_liquid_fish taken as NA for the dry cooking category

Figure 3A

Estimates at cooking times of 2, 10 and 25 min

time_mm <-marginal_means(full_model_org_units, data = dat, mod = "1", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
 
p_time_mm<-my_orchard(time_mm, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
           scale_size_continuous(range = c(1, 10))+
           scale_fill_manual(values="gray75")+
           scale_colour_manual(values = "gray60")+ # change colours
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 24), # change font sizes
                 legend.title = element_text(size = 13),
                 legend.text = element_text(size = 10),
                 legend.position = c(0,0.13))+
          guides(size=F)

Figure 3B

Estimates at 0 mL/g of tissue, 10 mL/g of tissue or 45 mL/g of tissue

volume_mm <-marginal_means(full_model_org_units_oil_water, data = dat_oil_water, mod = "1", at = list(log_Ratio_liquid_fish= c(-2.3, 2.3, 3.8)), by = "log_Ratio_liquid_fish")
 
p_volume_mm<-my_orchard(volume_mm, xlab = "lnRR", condition.lab = "ln (Liquid to sample ratio)", alpha=0.3)+
           scale_size_continuous(range = c(1, 10))+
           scale_fill_manual(values="gray75")+
           scale_colour_manual(values = "gray60")+ # change colours
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 24), # change font sizes
                 legend.title = element_text(size = 13),
                 legend.text = element_text(size = 10),
                 legend.position = c(0,0.13))+
           guides(size=F)

Figure 3C

Estimates at cooking times of 2, 10 and 25 min

time_mm_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
 
p_time_mm_cat<-my_orchard(time_mm_cat, xlab = "lnRR", condition.lab  = "Cooking time (sec)", alpha=0.3)+
           scale_size_continuous(range = c(1, 10))+
           scale_fill_manual(values=c("goldenrod2", "dodgerblue3"))+
           scale_colour_manual(values = c("goldenrod2", "dodgerblue3"))+ # change colours
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 24), # change font sizes
                 legend.title = element_text(size = 13),
                 legend.text = element_text(size = 10),
                 legend.position = c(0,0.12))

Combine plots and save

((p_time_mm/p_volume_mm) | p_time_mm_cat) + plot_annotation(tag_levels = c("A", "B",
    "C"))

ggsave("fig/Fig_3.png", width = 14, height = 10, dpi = 1200)

Models with Ratio_liquid_fish taken as 0 for the dry cooking category

Figure 3A

Estimates at cooking times of 2, 10 and 25 min

time_mm0 <-marginal_means(full_model_org_units0, data = dat, mod = "1", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
 
p_time_mm0<-my_orchard(time_mm0, xlab = "lnRR", condition.lab = "Cooking time (sec)", alpha=0.3)+
           scale_size_continuous(range = c(1, 10))+
           scale_fill_manual(values="gray75")+
           scale_colour_manual(values = "gray60")+ # change colours
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 24), # change font sizes
                 legend.title = element_text(size = 13),
                 legend.text = element_text(size = 10),
                 legend.position = c(0,0.25))+
           ylim(-6.05, 3)+
           annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 431), 
            parse = TRUE, hjust = "right", size = 3.5)

Figure 3B

Estimates at 0.1 mL/g of tissue, 10 mL/g of tissue or 45 mL/g of tissue

volume_mm0 <-marginal_means(full_model_org_units0, data = dat, mod = "1", at = list(log_Ratio_liquid_fish0= c(0, 2.4, 3.8)), by = "log_Ratio_liquid_fish0")
 
p_volume_mm0<-my_orchard(volume_mm0, xlab = "lnRR", condition.lab = "ln (Liquid to sample ratio + 1)", alpha=0.3)+
           scale_size_continuous(range = c(1, 10))+
           scale_fill_manual(values="gray75")+
           scale_colour_manual(values = "gray60")+ # change colours
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 24), # change font sizes
                 legend.title = element_text(size = 13),
                 legend.text = element_text(size = 10),
                 legend.position = c(0,0.13))+
          guides(size=F)+
           ylim(-6.05, 3)+
           annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 431), 
            parse = TRUE, hjust = "right", size = 3.5)

Figure 3C

Estimates at cooking times of 2, 10 and 25 min

In this case, water- and oil-based cooking must be separated from dry cooking to avoid extrapolations of the dry cooking effect sizes at the mean liquid ratio.

time_mm_cat <- marginal_means(full_model_org_units, data = dat, mod = "Cooking_Category", at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
 
p_time_mm_wat_oil<-my_orchard(time_mm_cat, xlab = "lnRR", condition.lab  = "Cooking time (sec)", alpha=0.3)+
           scale_size_continuous(range = c(1, 10))+
           scale_fill_manual(values=c("goldenrod2", "dodgerblue3"))+
           scale_colour_manual(values = c("goldenrod2", "dodgerblue3"))+ # change colours
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 0), # change font sizes
                 legend.title = element_text(size = 12),
                 legend.text = element_text(size = 10),
                 legend.position = "none")+ 
           guides(shape=F, size=F)+
           ylim(-6.1, 3)+
           annotate("text", y = 1.9, x = (seq(1, 2, 1) + 
            0.3), label = paste("italic(k)==", c(263, 121)), 
            parse = TRUE, hjust = "right", size = 3.5)

time_mm_dry<-marginal_means(full_model_org_units_dry, data = dat, at = list(Length_cooking_time_in_s = c(120,600,1500)), by = "Length_cooking_time_in_s")
p_time_mm_dry<-my_orchard(time_mm_dry, xlab = "lnRR", condition.lab  = "Cooking time (sec)", alpha=0.3)+
           scale_size_continuous(range = c(1, 10))+
           scale_fill_manual(values=c("#55C667FF"))+
           scale_colour_manual(values = c("#55C667FF"))+ # change colours
           theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
                 text = element_text(size = 24), # change font sizes
                 legend.title = element_text(size = 12),
                 legend.text = element_text(size = 10),
                 legend.position = c(0.02,0.14),
                 legend.margin=margin(1,1,1,1))+ 
           guides(size=F)+
           ylim(-6.05, 3)+
           annotate("text", y = 1.9, x = 1.3, label = paste("italic(k)==", 47), 
            parse = TRUE, hjust = "right", size = 3.5)


p_time_mm_cat<-p_time_mm_wat_oil/p_time_mm_dry + plot_layout(heights=c(2,1))

Combine plots and save

((p_time_mm0/p_volume_mm0) | p_time_mm_cat) + plot_annotation(tag_levels = c("A",
    "B", "C"))

ggsave("fig/Fig_3_zero_ratio.png", width = 14, height = 11, dpi = 1200)

Figure 4, with Ratio_liquid_fish taken as NA for the dry cooking category

Figure 4A

##### Oil based
full_model_oil_time<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Ratio_liquid_fish)))

pred_oil_time<-predict.rma(full_model_oil_time, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time<-as.data.frame(pred_oil_time)
pred_oil_time$Length_cooking_time_in_s=pred_oil_time$X.Length_cooking_time_in_s
pred_oil_time<-left_join(oil_dat, pred_oil_time, by="Length_cooking_time_in_s")


##### Water based
full_model_water_time<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Ratio_liquid_fish)))

pred_water_time<-predict.rma(full_model_water_time, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time<-as.data.frame(pred_water_time)
pred_water_time$Length_cooking_time_in_s=pred_water_time$X.Length_cooking_time_in_s
pred_water_time<-left_join(water_dat, pred_water_time, by="Length_cooking_time_in_s")

##### No liquid 

full_model_dry_time<- run_model_dry(dry_dat, ~ Length_cooking_time_in_s)

pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")




p_4A<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
  
       geom_ribbon(data=pred_water_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_time,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
       geom_ribbon(data=pred_oil_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_oil_time,aes(y = pred), size = 1.5, col="goldenrod")+  
  
        geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(1,0), 
          legend.justification = c(1,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))+
  guides(fill=F)

Figure 4B

##### Oil based
full_model_oil_vol <- run_model_oil(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
    scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)
pred_oil_vol <- predict.rma(full_model_oil_vol, addx = TRUE, newmods = cbind(0, 0,
    0, oil_dat$log_Ratio_liquid_fish))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_oil_vol <- as.data.frame(pred_oil_vol)
pred_oil_vol <- pred_oil_vol %>%
    mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "oil-based",
        lnRR = 0)  # for the plot to work, we need to add a column with cooking category and a column with lnRR


##### Water based

full_model_water_vol <- run_model_water(water_dat, ~scale(Temperature_in_Celsius) +
    scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish)

pred_water_vol <- predict.rma(full_model_water_vol, addx = TRUE, newmods = cbind(0,
    0, water_dat$log_Ratio_liquid_fish))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_water_vol <- as.data.frame(pred_water_vol)
pred_water_vol <- pred_water_vol %>%
    mutate(Ratio_liquid_fish = exp(X.log_Ratio_liquid_fish), Cooking_Category = "water-based",
        lnRR = 0)



p_4B <- ggplot(dat, aes(x = log(Ratio_liquid_fish), y = lnRR, fill = Cooking_Category)) +

geom_ribbon(data = pred_water_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.2) + geom_line(data = pred_water_vol, aes(y = pred), size = 1.5, col = "dodgerblue") +

geom_ribbon(data = pred_oil_vol, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
    geom_line(data = pred_oil_vol, aes(y = pred), size = 1.5, col = "goldenrod") +


geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio) (mL/g)",
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
        legend.position = "none", panel.border = element_rect(colour = "black", fill = NA,
            size = 1.2))  #### The line doesn't go all the way down for water-based because the highest values are not included in the full model

Figure 4C

full_model_oil_temp<- run_model_oil(oil_dat, ~ Temperature_in_Celsius +
                                           scale(Length_cooking_time_in_s)+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Ratio_liquid_fish)))

pred_oil_temp<-predict.rma(full_model_oil_temp, addx=TRUE, newmods=cbind(oil_dat$Temperature_in_Celsius,0, 0,0)) 
pred_oil_temp<-as.data.frame(pred_oil_temp)
pred_oil_temp$Temperature_in_Celsius=pred_oil_temp$X.Temperature_in_Celsius
pred_oil_temp<-left_join(oil_dat, pred_oil_temp, by="Temperature_in_Celsius")



p_4C<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR, fill=Cooking_Category)) +
    
       geom_ribbon(data=pred_oil_temp, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=pred_oil_temp,aes(y = pred), size = 1.5, col="goldenrod")+  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(1,0), 
          legend.justification = c(1,0),
          legend.background = element_blank(), 
          legend.direction="vertical",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))+
  guides(size=F)

Figure 4D

##### Oil based
full_model_oil_PFAS<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Ratio_liquid_fish)))

pred_oil_PFAS<-predict.rma(full_model_oil_PFAS, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS<-as.data.frame(pred_oil_PFAS)
pred_oil_PFAS$PFAS_carbon_chain=pred_oil_PFAS$X.PFAS_carbon_chain
pred_oil_PFAS<-left_join(oil_dat, pred_oil_PFAS, by="PFAS_carbon_chain")


##### Water based
full_model_water_PFAS<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Ratio_liquid_fish)))

pred_water_PFAS<-predict.rma(full_model_water_PFAS, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS<-as.data.frame(pred_water_PFAS)
pred_water_PFAS$PFAS_carbon_chain=pred_water_PFAS$X.PFAS_carbon_chain
pred_water_PFAS<-left_join(water_dat, pred_water_PFAS, by="PFAS_carbon_chain")

##### No liquid 

full_model_dry_PFAS<- run_model_dry(dry_dat, ~ PFAS_carbon_chain)

pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")




p_4D<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
  
    
       geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_ribbon(data=pred_water_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_PFAS,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
  
       geom_ribbon(data=pred_oil_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=pred_oil_PFAS,aes(y = pred), size = 1.5, col="goldenrod")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Combine and save

(p_4A + p_4B)/(p_4C + p_4D) + plot_annotation(tag_levels = c("A", "B", "C", "D"))

ggsave("fig/Fig_4.png", width = 15, height = 12, dpi = 1200)

Figure 4, with Ratio_liquid_fish taken as 0 for the dry cooking category

Figure 4A

##### Oil based
full_model_oil_time0<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Ratio_liquid_fish_0 + 1)))

pred_oil_time0<-predict.rma(full_model_oil_time0, addx=TRUE, newmods=cbind(0,oil_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_oil_time0<-as.data.frame(pred_oil_time0)
pred_oil_time0$Length_cooking_time_in_s=pred_oil_time0$X.Length_cooking_time_in_s
pred_oil_time0<-left_join(oil_dat, pred_oil_time0, by="Length_cooking_time_in_s")


##### Water based
full_model_water_time0<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
                                           Length_cooking_time_in_s+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Ratio_liquid_fish_0 + 1)))

pred_water_time0<-predict.rma(full_model_water_time0, addx=TRUE, newmods=cbind(water_dat$Length_cooking_time_in_s, 0, 0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_time0<-as.data.frame(pred_water_time0)
pred_water_time0$Length_cooking_time_in_s=pred_water_time0$X.Length_cooking_time_in_s
pred_water_time0<-left_join(water_dat, pred_water_time0, by="Length_cooking_time_in_s")

##### No liquid 

full_model_dry_time<- run_model_dry(dry_dat, ~ Length_cooking_time_in_s)

pred_dry_time<-predict.rma(full_model_dry_time, addx=TRUE) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_dry_time<-as.data.frame(pred_dry_time)
pred_dry_time$Length_cooking_time_in_s=pred_dry_time$X.Length_cooking_time_in_s
pred_dry_time<-left_join(dry_dat, pred_dry_time, by="Length_cooking_time_in_s")




p_4A0<-ggplot(dat,aes(x = Length_cooking_time_in_s, y = lnRR, fill=Cooking_Category)) +
  
       geom_ribbon(data=pred_water_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_time0,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
       geom_ribbon(data=pred_oil_time0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_oil_time0,aes(y = pred), size = 1.5, col="goldenrod")+  
  
        geom_ribbon(data=pred_dry_time, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_time,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "Cooking time (s)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(1,0), 
          legend.justification = c(1,0),
          legend.background = element_blank(), 
          legend.direction="horizontal",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))+
  guides(fill=F)

Figure 4B

##### Oil based
full_model_oil_vol0 <- run_model_oil(oil_dat, ~scale(Temperature_in_Celsius) + scale(Length_cooking_time_in_s) +
    scale(PFAS_carbon_chain) + log_Ratio_liquid_fish0)
pred_oil_vol0 <- predict.rma(full_model_oil_vol0, addx = TRUE, newmods = cbind(0,
    0, 0, oil_dat$log_Ratio_liquid_fish0))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_oil_vol0 <- as.data.frame(pred_oil_vol0)
pred_oil_vol0 <- pred_oil_vol0 %>%
    mutate(Ratio_liquid_fish_0 = exp(X.log_Ratio_liquid_fish0) - 1, Cooking_Category = "oil-based",
        lnRR = 0)  # for the plot to work, we need to add a column with cooking category and a column with lnRR


##### Water based

full_model_water_vol0 <- run_model_water(water_dat, ~scale(Temperature_in_Celsius) +
    scale(Length_cooking_time_in_s) + scale(PFAS_carbon_chain) + log_Ratio_liquid_fish0)

pred_water_vol0 <- predict.rma(full_model_water_vol0, addx = TRUE, newmods = cbind(0,
    0, water_dat$log_Ratio_liquid_fish0))  # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time

pred_water_vol0 <- as.data.frame(pred_water_vol0)
pred_water_vol0 <- pred_water_vol0 %>%
    mutate(Ratio_liquid_fish_0 = exp(X.log_Ratio_liquid_fish0) - 1, Cooking_Category = "water-based",
        lnRR = 0)



p_4B0 <- ggplot(dat, aes(x = log(Ratio_liquid_fish_0 + 1), y = lnRR, fill = Cooking_Category)) +

geom_ribbon(data = pred_water_vol0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.2) + geom_line(data = pred_water_vol0, aes(y = pred), size = 1.5, col = "dodgerblue") +

geom_ribbon(data = pred_oil_vol0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL),
    alpha = 0.3) + geom_line(data = pred_oil_vol0, aes(y = pred), size = 1.5, col = "goldenrod") +


geom_point(aes(size = (1/sqrt(var_lnRR)), fill = Cooking_Category), shape = 21, alpha = 0.8) +
    scale_fill_manual(values = c("#55C667FF", "goldenrod2", "dodgerblue3")) + labs(x = "ln(Liquid volume to tissue sample ratio + 1) (mL/g)",
    y = "lnRR", size = "Precison (1/SE)") + scale_size_continuous(range = c(1, 10)) +
    theme_bw() + geom_hline(yintercept = 0, linetype = 2, colour = "black", alpha = 0.5) +
    theme(text = element_text(size = 18, colour = "black", hjust = 0.5), legend.text = element_text(size = 14),
        legend.position = "none", panel.border = element_rect(colour = "black", fill = NA,
            size = 1.2))

Figure 4C

full_model_oil_temp0<- run_model_oil(oil_dat, ~ Temperature_in_Celsius +
                                           scale(Length_cooking_time_in_s)+
                                           scale(PFAS_carbon_chain) +
                                           scale(log(Ratio_liquid_fish_0 + 1)))

pred_oil_temp0<-predict.rma(full_model_oil_temp0, addx=TRUE, newmods=cbind(oil_dat$Temperature_in_Celsius,0, 0,0)) 
pred_oil_temp0<-as.data.frame(pred_oil_temp0)
pred_oil_temp0$Temperature_in_Celsius=pred_oil_temp0$X.Temperature_in_Celsius
pred_oil_temp0<-left_join(oil_dat, pred_oil_temp0, by="Temperature_in_Celsius")



p_4C0<-ggplot(dat,aes(x = Temperature_in_Celsius, y = lnRR, fill=Cooking_Category)) +
    
       geom_ribbon(data=pred_oil_temp0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=pred_oil_temp0,aes(y = pred), size = 1.5, col="goldenrod")+  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "Cooking temperature (°C)", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(1,0), 
          legend.justification = c(1,0),
          legend.background = element_blank(), 
          legend.direction="vertical",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))+
  guides(size=F)

Figure 4D

##### Oil based
full_model_oil_PFAS0<- run_model_oil(oil_dat, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Ratio_liquid_fish_0 + 1)))

pred_oil_PFAS0<-predict.rma(full_model_oil_PFAS0, addx=TRUE, newmods=cbind(0,0, oil_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of PFAS carbon chain
pred_oil_PFAS0<-as.data.frame(pred_oil_PFAS0)
pred_oil_PFAS0$PFAS_carbon_chain=pred_oil_PFAS0$X.PFAS_carbon_chain
pred_oil_PFAS0<-left_join(oil_dat, pred_oil_PFAS0, by="PFAS_carbon_chain")


##### Water based
full_model_water_PFAS0<- run_model_water(water_dat, ~ scale(Temperature_in_Celsius) +
                                           scale(Length_cooking_time_in_s)+
                                           PFAS_carbon_chain +
                                           scale(log(Ratio_liquid_fish_0 + 1)))

pred_water_PFAS0<-predict.rma(full_model_water_PFAS0, addx=TRUE, newmods=cbind(0, water_dat$PFAS_carbon_chain,0)) # Set all predictors to their mean (mean =0 when z-transformed) and set the range of values of cooking time
pred_water_PFAS0<-as.data.frame(pred_water_PFAS0)
pred_water_PFAS0$PFAS_carbon_chain=pred_water_PFAS0$X.PFAS_carbon_chain
pred_water_PFAS0<-left_join(water_dat, pred_water_PFAS0, by="PFAS_carbon_chain")

##### No liquid 

full_model_dry_PFAS<- run_model_dry(dry_dat, ~ PFAS_carbon_chain)

pred_dry_PFAS<-predict.rma(full_model_dry_PFAS, addx=TRUE)
pred_dry_PFAS<-as.data.frame(pred_dry_PFAS)
pred_dry_PFAS$PFAS_carbon_chain=pred_dry_PFAS$X.PFAS_carbon_chain
pred_dry_PFAS<-left_join(dry_dat, pred_dry_PFAS, by="PFAS_carbon_chain")




p_4D0<-ggplot(dat,aes(x = PFAS_carbon_chain, y = lnRR, fill=Cooking_Category)) +
  
    
       geom_ribbon(data=pred_dry_PFAS, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_dry_PFAS,aes(y = pred), size = 1.5, col="palegreen3")+  
  
  
       geom_ribbon(data=pred_water_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.2) +
       geom_line(data=pred_water_PFAS0,aes(y = pred), size = 1.5, col="dodgerblue")+  
  
  
       geom_ribbon(data=pred_oil_PFAS0, aes(ymin = ci.lb, ymax = ci.ub, color = NULL), alpha = 0.3) +
       geom_line(data=pred_oil_PFAS0,aes(y = pred), size = 1.5, col="goldenrod")+  
  
  
       geom_point(aes(size=(1/sqrt(var_lnRR)), fill=Cooking_Category), shape=21, alpha=0.8) +
       scale_fill_manual(values=c("#55C667FF", "goldenrod2", "dodgerblue3"))+
       labs(x = "PFAS carbon chain length", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 18, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))

Combine and save

(p_4A0 + p_4B0)/(p_4C0 + p_4D0) + plot_annotation(tag_levels = c("A", "B", "C", "D"))

ggsave("fig/Fig_4_zero_ratio.png", width = 15, height = 12, dpi = 1200)

Figure 5, with Ratio_liquid_fish taken as NA for the dry cooking category

Figure 5A

dat$Study_ID<- as.factor(dat$Study_ID)

# funnel(full_model, 
#       yaxis="seinv", # Inverse of standard error (precision) as the y axis
#       level = c(90, 95, 99),  # levels of statistical significance highlighted 
#       shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
#       legend = TRUE, # display legend
#       ylab="Precision (1/SE)", 
#       cex.lab=1.75, 
#       digits=1, 
#       cex=2,
#       pch=21,
#       col=dat$Study_ID)


pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,6,0.1,0))

plot_f <- funnel(full_model, 
      yaxis="seinv", # Inverse of standard error (precision) as the y axis
      level = c(90, 95, 99),  # levels of statistical significance highlighted 
      shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
      legend = TRUE, # display legend
      ylab="Precision (1/SE)", 
      cex.lab=1.75, 
      digits=1, 
      ylim=c(0.82,0.94),
      xlim=c(-6, 6),
      cex=2,
      pch=21,
      col=dat$Study_ID)

p_5A <- recordPlot(plot_f)
invisible(dev.off())

Figure 5B

full_model_egger <- run_model(dat, ~ - 1 +
                      I(sqrt(1/N_tilde)) +  
                      scale(Publication_year) + 
                      scale(Temperature_in_Celsius) +
                      scale(Length_cooking_time_in_s) +
                      scale(PFAS_carbon_chain) +
                      scale(log(Ratio_liquid_fish))) # Model to get predictions


pred_egger<-predict.rma(full_model_egger, addx=TRUE, newmods=cbind(sqrt(1/dat$N_tilde),0,0,0 ,0, 0)) 
pred_egger<-as.data.frame(pred_egger)
pred_egger$SE_eff_N=pred_egger$X.I.sqrt.1.N_tilde..
pred_egger<- pred_egger %>% mutate(N_tilde = ((1/X.I.sqrt.1.N_tilde..)^2), lnRR = 0) 

p_5B<-ggplot(dat,aes(x = sqrt(1/N_tilde), y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_egger, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_egger,aes(y = pred), size = 1.5, color="orangered2")+  

       labs(x = "Standard error", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))+
  xlim(0.18,1)

Figure 5C

full_model_pub <- run_model(dat, ~ - 1 +
                      scale(I(sqrt(1/N_tilde))) +  
                      Publication_year + 
                      scale(Temperature_in_Celsius) +
                      scale(Length_cooking_time_in_s) +
                      scale(PFAS_carbon_chain) +
                      scale(log(Ratio_liquid_fish))) # Model to get predictions


pred_pub<-predict.rma(full_model_pub, addx=TRUE, newmods=cbind(0,dat$Publication_year,0,0 ,0, 0)) 
pred_pub<-as.data.frame(pred_pub)
pred_pub$Publication_year=pred_pub$X.Publication_year
pred_pub<-left_join(dat, pred_pub, by="Publication_year")



p_5C<-ggplot(dat,aes(x = Publication_year, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_pub, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_pub,aes(y = pred), size = 1.5, color="orangered2")+  

       labs(x = "Publication year", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(1,0), 
          legend.justification = c(1,0),
          legend.background = element_blank(), 
          legend.direction="vertical",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))  +
   scale_x_continuous(breaks=c(2008, 2010, 2012, 2014, 2016, 2018 ,2020))

Combine and save

(ggdraw(p_5A) + ggdraw(p_5B) + ggdraw(p_5C) + plot_annotation(tag_levels = "A"))

ggsave(here("fig/Fig_5BC.png"), width = 18, height = 7, dpi = 1200)

Figure 5, with Ratio_liquid_fish taken as 0 for the dry cooking category

Figure 5A

dat$Study_ID<- as.factor(dat$Study_ID)

# funnel(full_model, 
#       yaxis="seinv", # Inverse of standard error (precision) as the y axis
#       level = c(90, 95, 99),  # levels of statistical significance highlighted 
#       shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
#       legend = TRUE, # display legend
#       ylab="Precision (1/SE)", 
#       cex.lab=1.75, 
#       digits=1, 
#       cex=2,
#       pch=21,
#       col=dat$Study_ID)


pdf(NULL)
dev.control(displaylist="enable")
par(mar=c(4,6,0.1,0))

plot_f0 <- funnel(full_model0, 
      yaxis="seinv", # Inverse of standard error (precision) as the y axis
      level = c(90, 95, 99),  # levels of statistical significance highlighted 
      shade = c("white", "gray75", "gray55", "gray40"), # shades for different levels of statistical significance
      legend = TRUE, # display legend
      ylab="Precision (1/SE)", 
      cex.lab=1.75, 
      digits=1, 
      ylim=c(0.82,0.94),
      xlim=c(-6, 6),
      cex=2,
      pch=21,
      col=dat$Study_ID)

p_5A0 <- recordPlot(plot_f0)
invisible(dev.off())

Figure 5B

full_model_egger0 <- run_model(dat, ~ - 1 +
                      I(sqrt(1/N_tilde)) +  
                      scale(Publication_year) + 
                      scale(Temperature_in_Celsius) +
                      scale(Length_cooking_time_in_s) +
                      scale(PFAS_carbon_chain) +
                      scale(log(Ratio_liquid_fish_0 + 1))) # Model to get predictions


pred_egger0<-predict.rma(full_model_egger0, addx=TRUE, newmods=cbind(sqrt(1/dat$N_tilde),0,0,0 ,0, 0)) 
pred_egger0<-as.data.frame(pred_egger0)
pred_egger0$SE_eff_N=pred_egger0$X.I.sqrt.1.N_tilde..
pred_egger0<- pred_egger0 %>% mutate(N_tilde = ((1/X.I.sqrt.1.N_tilde..)^2), lnRR = 0) 

p_5B0<-ggplot(dat,aes(x = sqrt(1/N_tilde), y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_egger0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_egger0,aes(y = pred), size = 1.5, color="orangered2")+  

       labs(x = "Standard error", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.position="none",
          panel.border=element_rect(colour="black", fill=NA, size=1.2))+
  xlim(0.18,1)

Figure 5C

full_model_pub0 <- run_model(dat, ~ - 1 +
                      scale(I(sqrt(1/N_tilde))) +  
                      Publication_year + 
                      scale(Temperature_in_Celsius) +
                      scale(Length_cooking_time_in_s) +
                      scale(PFAS_carbon_chain) +
                      scale(log(Ratio_liquid_fish_0 + 1))) # Model to get predictions


pred_pub0<-predict.rma(full_model_pub0, addx=TRUE, newmods=cbind(0,dat$Publication_year,0,0 ,0, 0)) 
pred_pub0<-as.data.frame(pred_pub0)
pred_pub0$Publication_year=pred_pub0$X.Publication_year
pred_pub0<-left_join(dat, pred_pub0, by="Publication_year")



p_5C0<-ggplot(dat,aes(x = Publication_year, y = lnRR)) +
  
       geom_point(aes(size=(1/sqrt(var_lnRR))), shape=21, alpha=0.8, fill="gray75") +
  
       geom_ribbon(data=pred_pub0, aes(ymin = ci.lb, ymax = ci.ub), alpha = 0.25, fill="orangered") +
       geom_line(data=pred_pub0,aes(y = pred), size = 1.5, color="orangered2")+  

       labs(x = "Publication year", y = "lnRR", size = "Precison (1/SE)") + 
  scale_size_continuous(range=c(1,10))+
  theme_bw() +
  geom_hline(yintercept = 0,linetype = 2, colour = "black",alpha=0.5)+   # horizontal line at lnRR = 0
  theme(text = element_text(size = 20, colour = "black", hjust = 0.5), # change font sizes and legend position
          legend.text=element_text(size=14),
          legend.position=c(1,0), 
          legend.justification = c(1,0),
          legend.background = element_blank(), 
          legend.direction="vertical",
          legend.title = element_text(size=15), 
          panel.border=element_rect(colour="black", fill=NA, size=1.2))  +
   scale_x_continuous(breaks=c(2008, 2010, 2012, 2014, 2016, 2018 ,2020))

Combine and save

(ggdraw(p_5A0) + ggdraw(p_5B0) + ggdraw(p_5C0) + plot_annotation(tag_levels = "A"))

ggsave(here("fig/Fig_5BC_zero_ratio.png"), width = 18, height = 7, dpi = 1200)

Software and packages versions

sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=English_Australia.1252  LC_CTYPE=English_Australia.1252   
## [3] LC_MONETARY=English_Australia.1252 LC_NUMERIC=C                      
## [5] LC_TIME=English_Australia.1252    
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] cowplot_1.1.1         GGally_2.1.2          kableExtra_1.3.4     
##  [4] emmeans_1.6.2-9990002 patchwork_1.1.1       clubSandwich_0.5.3   
##  [7] ape_5.5               orchaRd_0.0.0.9000    metaAidR_0.0.0.9000  
## [10] metafor_3.0-2         Matrix_1.3-4          here_1.0.1           
## [13] googlesheets4_1.0.0   forcats_0.5.1         stringr_1.4.0        
## [16] dplyr_1.0.7           purrr_0.3.4           readr_2.0.0          
## [19] tidyr_1.1.3           tibble_3.1.3          ggplot2_3.3.5        
## [22] tidyverse_1.3.1      
## 
## loaded via a namespace (and not attached):
##  [1] TH.data_1.0-10     googledrive_2.0.0  ggbeeswarm_0.6.0   colorspace_2.0-2  
##  [5] ellipsis_0.3.2     rprojroot_2.0.2    estimability_1.3   fs_1.5.0          
##  [9] rstudioapi_0.13    farver_2.1.0       fansi_0.5.0        mvtnorm_1.1-2     
## [13] lubridate_1.7.10   mathjaxr_1.4-0     xml2_1.3.2         codetools_0.2-18  
## [17] splines_4.1.0      knitr_1.34         jsonlite_1.7.2     broom_0.7.9       
## [21] dbplyr_2.1.1       compiler_4.1.0     httr_1.4.2         backports_1.2.1   
## [25] assertthat_0.2.1   gargle_1.2.0       cli_3.0.1          formatR_1.11      
## [29] htmltools_0.5.1.1  tools_4.1.0        coda_0.19-4        gtable_0.3.0      
## [33] glue_1.4.2         Rcpp_1.0.7         cellranger_1.1.0   jquerylib_0.1.4   
## [37] vctrs_0.3.8        svglite_2.0.0      nlme_3.1-152       xfun_0.24         
## [41] rvest_1.0.1        lifecycle_1.0.0    MASS_7.3-54        zoo_1.8-9         
## [45] scales_1.1.1       hms_1.1.0          parallel_4.1.0     sandwich_3.0-1    
## [49] RColorBrewer_1.1-2 yaml_2.2.1         sass_0.4.0         reshape_0.8.8     
## [53] stringi_1.7.3      highr_0.9          rlang_0.4.11       pkgconfig_2.0.3   
## [57] systemfonts_1.0.2  evaluate_0.14      lattice_0.20-44    labeling_0.4.2    
## [61] tidyselect_1.1.1   plyr_1.8.6         magrittr_2.0.1     bookdown_0.22     
## [65] R6_2.5.1           generics_0.1.0     multcomp_1.4-17    DBI_1.1.1         
## [69] pillar_1.6.2       haven_2.4.3        withr_2.4.2        survival_3.2-11   
## [73] modelr_0.1.8       crayon_1.4.1       utf8_1.2.2         tzdb_0.1.2        
## [77] rmarkdown_2.11     grid_4.1.0         readxl_1.3.1       rmdformats_1.0.2  
## [81] reprex_2.0.1       digest_0.6.27      webshot_0.5.2      xtable_1.8-4      
## [85] munsell_0.5.0      beeswarm_0.4.0     viridisLite_0.4.0  vipor_0.4.5       
## [89] bslib_0.2.5.1